Bridging the Gap Between Science and Clinical Efficacy: Physiology, Imaging, and Modeling of Aerosols in the Lung
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Development of a new drug for the treatment of lung disease is a complex and time consuming process involving numerous disciplines of basic and applied sciences. During the 2015 Congress of the International Society for Aerosols in Medicine, a group of experts including aerosol scientists, physiologists, modelers, imagers, and clinicians participated in a workshop aiming at bridging the gap between basic research and clinical efficacy of inhaled drugs. This publication summarizes the current consensus on the topic. It begins with a short description of basic concepts of aerosol transport and a discussion on targeting strategies of inhaled aerosols to the lungs. It is followed by a description of both computational and biological lung models, and the use of imaging techniques to determine aerosol deposition distribution (ADD) in the lung. Finally, the importance of ADD to clinical efficacy is discussed. Several gaps were identified between basic science and clinical efficacy. One gap between scientific research aimed at predicting, controlling, and measuring ADD and the clinical use of inhaled aerosols is the considerable challenge of obtaining, in a single study, accurate information describing the optimal lung regions to be targeted, the effectiveness of targeting determined from ADD, and some measure of the drug's effectiveness. Other identified gaps were the language and methodology barriers that exist among disciplines, along with the significant regulatory hurdles that need to be overcome for novel drugs and/or therapies to reach the marketplace and benefit the patient. Despite these gaps, much progress has been made in recent years to improve clinical efficacy of inhaled drugs. Also, the recent efforts by many funding agencies and industry to support multidisciplinary networks including basic science researchers, R&D scientists, and clinicians will go a long way to further reduce the gap between science and clinical efficacy.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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