<i>In Vivo–In Vitro</i> Correlations: Predicting Pulmonary Drug Deposition from Pharmaceutical Aerosols
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".