Dose Emission Characteristics of Placebo PulmoSphere <sup>®</sup> Particles Are Unaffected by a Subject's Inhalation Maneuver
Bibliographic record
Abstract
BACKGROUND: Good compliance to the prescribed dosing regimen and inhaler instructions for use are critical for asthma/chronic obstructive pulmonary disease (COPD) patients to achieve good control of their disease. We investigated the extent to which a system comprising porous particles delivered with a passive dry powder inhaler could be designed to achieve significant reductions in dose inhalation errors. METHODS: Porous placebo particles were prepared by an emulsion-based spray-drying method (PulmoSphere® technology). The formulations were administered as dry powders with a portable, blister-based dry powder inhaler (Simoon Inhaler). The inhalation profiles of 69 asthma/COPD subjects were determined with an inhaler simulator with resistance comparable to that of the Simoon Inhaler. Powder emptying from the device was assessed by laser photometry. Aerosol performance was assessed on a Next Generation Impactor, and with the idealized Alberta mouth-throat model using both square-wave and subject-inhalation profiles generated in the breathing study. RESULTS: Virtually all subjects could achieve a pressure drop of at least 1 kPa and an inhaled volume of at least 500 mL with the Simoon Inhaler. In vitro measures of particle deposition were found to be largely independent of the inhalation maneuver (flow rate, inhaled volume, ramp time) across the broad range of inhalation profiles observed in the breathing study. The rapid emptying of powder from the Simoon Inhaler minimizes the impact of dose-related errors, such as failure to exhale before inhalation and failure to breath-hold post inhalation. CONCLUSIONS: Inertial impaction that is largely independent of a subject's inhalation maneuver can be achieved with a drug/device combination product comprising a porous particle formulation and blister-based inhaler.
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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 | 0.001 |
| 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 | 0.000 |
| 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".