<i>In vitro</i>and<i>in vivo</i>testing methods for respiratory drug delivery
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Successful respiratory drug delivery for local and systemic purposes is predicated on the availability of in vitro and in vivo methods for determining drug delivery and disposition following respiratory administration. AREAS COVERED IN THIS REVIEW: In this review, the relevance of new in vitro and in vivo methods for screening respiratory drug delivery is discussed. Specific topics covered include in vitro particle size characterization, in vitro dissolution test methods for respiratory formulations and in vitro respiratory absorption and disposition screening methods. Furthermore, in vivo respiratory dosing methods, in vivo respiratory aerosol deposition and drug absorption screening methods, and correlation between in vitro and in vivo methods are reviewed. WHAT THE READER WILL GAIN: After reading this article, the reader will have an enriched knowledge regarding the various in vitro and in vivo testing methods for respiratory drug delivery. Most importantly, this paper will make it possible for readers to appreciate the strengths and weaknesses of each test method, which in turn will assist them in selecting specific methods that suit their scientific needs. TAKE HOME MESSAGE: New in vitro and in vivo methods for screening respiratory drug delivery are indispensible, especially from the respiratory drug development and quality control perspective. Each method has unique advantages and disadvantages that influence method selection and data interpretation. Although in vitro methods are used during drug development, they augment rather than substitute in vivo methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".