<i>In Vivo–In Vitro</i> Correlations: Predicting Pulmonary Drug Deposition from Pharmaceutical Aerosols
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Bibliographic record
Abstract
In order to answer the question "what research remains to be done?" we review the current state of the art in pharmaceutical aerosol deposition modeling and explore possible in vivo- in vitro correlations (IVIVC) linking drug deposition in the human lung to predictions made using in vitro physical airway models and in silico computer models. The use of physical replicas of portions of the respiratory tract is considered, alongside the advantages and disadvantages of the different imaging methods used to obtain their dimensions. The use of airway replicas to determine drug deposition in vitro is discussed and compared with the predictions from different empirical curve fits to long-standing in vivo deposition data for monodisperse aerosols. The use of improved computational models and three-dimensional computational fluid dynamics (CFD) to predict aerosol deposition within the respiratory tract is examined. CFD's ability to predict both drug deposition from pharmaceutical aerosol sprays and powder behavior in dry powder inhalers is examined; both were highlighted as important areas for future research. Although the authors note the abilities of current in vitro and in silico methods to predict in vivo data, a number of limitations remain. These include our present inability to either image or replicate all but the most proximal airways in sufficient spatial and temporal detail to allow full capture of the fluid and aerosol mechanics in these regions. In addition, the highly complex microscale behavior of aerosols within inhalers and the respiratory tract places extreme computational demands on in silico methods. When the complexity of variations in respiratory tract geometry is associated with additional factors such as breathing pattern, age, disease state, postural position, and patient-device interaction are all considered, it is clear that further research is required before the prediction of all aspects of inhaled pharmaceutical aerosol deposition is possible.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 it