Considerations for Designing <i>In Vitro</i> Bioequivalence (IVBE) Studies for Pressurized Metered Dose Inhalers (pMDIs) with Spacer or Valved Holding Chamber (S/VHC) Add-on Devices
Bibliographic record
Abstract
BACKGROUND: The choice of analytical test methods and associated statistical considerations are considered for the laboratory testing of pressurized metered dose inhaler-spacer/valved holding chamber (pMDI-S/VHC) combinations for in vitro bioequivalence (IVBE). METHODS: Four scenarios are presented for comparing TEST ("second entry" or "generic") versus REF ("innovator"): (1) innovator and second entry product pMDI alone without any S/VHC (baseline comparison); (2) innovator and second entry pMDI product with the same S/VHC; (3) innovator pMDI product with existing S/VHC and second entry product with a different S/VHC; and (4) introduction of a second, different S/VHC to be used with a given innovator pMDI product. The following aspects should be reviewed in the preparatory stage of designing experiments to establish IVBE: (a) the inclusion of delayed inhalation; (b) the utilization of age-appropriate flow rates; and (c) the use of anatomically appropriate face models for evaluation of devices with a facemask. Statistical considerations that fit in with such experimental methods include: selection of pMDI batches and S/VHC lots; choice of sample size and acceptance criteria; bracketing or worst case approaches; and balanced/paired designs. A stepwise approach for selection of impactor stage groupings is presented, and an approach to determine realistic acceptance criteria based on REF product characteristics is suggested. RESULTS: An example of an efficient statistical design of experiment is provided for each scenario, together with alternate approaches for calculation of confidence intervals for the mean TEST/REF relationship. It is important to appreciate that the optimal design depends on balancing numerous considerations and will thus likely differ from case to case; hence, the designs presented here should be seen as illustrations rather than the only option available. More effective approaches may be found that suit a particular case at hand. CONCLUSIONS: The information provided will assist in developing correlations in support of IVBE for these add-on devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".