Testing Drugs in Animal Models of Cigarette Smoke–induced Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Animal models of cigarette smoke-induced chronic obstructive pulmonary disease (COPD) provide potentially useful ways to test drug therapies, either by direct administration of the treatment of interest, or by use of genetically modified animals that mimic the actions of the drug of interest. Evaluation of the potential effects of a drug in animal models requires a long-term (generally 6-mo) smoke exposure to produce/prevent lesions because acute models do not completely predict chronic events. There are now more than 30 chronic studies in the literature which, in aggregate, show that antiproteolytic therapies, antiinflammatory therapies, and antioxidant therapies substantially or completely prevent emphysema, small airway remodeling, and pulmonary hypertension in laboratory animals. However, the few corresponding trials in humans (anti-TNF-alpha therapy, PDE4 inhibitors) have produced only minor improvements or failed to prevent disease progression. New data from our laboratory indicates that, at least for murine emphysema, the development of disease goes through different phases, with early repair and late failure to repair smoke-induced damage. These observations suggest that the potential effects of drug treatment in humans may vary depending on the stage of the disease and that treatment may be more effective in relatively early disease. An additional complicating factor is that interventions that ameliorate emphysema may or may not prevent small airway remodeling and/or pulmonary hypertension, suggesting that different therapeutic approaches may be needed for the various different anatomic lesions of COPD.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".