Deposition, Imaging, and Clearance: What Remains to be Done?
Bibliographic record
Abstract
Deposition and clearance studies are used during product development and in fundamental research. These studies mostly involve radionuclide imaging, but pharmacokinetic methods are also used to assess the amount of drug absorbed through the lungs, which is closely related to lung deposition. Radionuclide imaging may be two-dimensional (gamma scintigraphy or planar imaging), or three-dimensional (single photon emission computed tomography and positron emission tomography). In October 2009, a group of scientists met at the "Thousand Years of Pharmaceutical Aerosols" conference in Reykjavik, Iceland, to discuss future research in key areas of pulmonary drug delivery. This article reports the session on "Deposition, imaging and clearance." The objective was partly to review our current understanding, but more importantly to assess "what remains to be done?" A need to standardize methodology and provide a regulatory framework by which data from radionuclide imaging methods could be compared between centers and used in the drug approval process was recognized. There is also a requirement for novel radiolabeling methods that are more representative of production processes for dry powder inhalers and pressurized metered dose inhalers. A need was identified for studies to aid our understanding of the relationship between clinical effects and regional deposition patterns of inhaled drugs. A robust methodology to assess clearance from small conducting airways should be developed, as a potential biomarker for therapies in cystic fibrosis and other diseases. The mechanisms by which inhaled nanoparticles are removed from the lungs, and the factors on which their removal depends, require further investigation. Last, and by no means least, we need a better understanding of patient-related factors, including how to reduce the variability in pulmonary drug delivery, in order to improve the precision of deposition and clearance measurements.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".