Considerations for the Conduct of Clinical Trials with Antiinflammatory Agents in Cystic Fibrosis. A Cystic Fibrosis Foundation Workshop Report
Bibliographic record
Abstract
Inflammation leads to lung destruction and loss of pulmonary function in patients with cystic fibrosis (CF). Drugs that modulate the cystic fibrosis transmembrane conductance regulator (CFTR) have recently been approved. Although the impact of CFTR modulators on sweat chloride and lung function are exciting, they have not yet demonstrated an effect on inflammation. Therefore, CF antiinflammatory drug development must continue. Unfortunately, the lack of clarity with this process has left investigators and industry sponsors frustrated. The Cystic Fibrosis Foundation established a working group in early 2014 to address this issue. There are many inflammatory processes disrupted in CF, and, therefore, there are many potential targets amenable to antiinflammatory therapy. Regardless of a drug's specific mechanism of action, it must ultimately affect the neutrophil or its products to impact CF. The working group concluded that before bringing new antiinflammatory drugs to clinical trial, preclinical safety studies must be conducted in disease-relevant models to assuage safety concerns. Furthermore, although studies of antiinflammatory therapies must first establish safety in adults, subsequent studies must involve children, as they are most likely to reap the most benefit. The working group also recommended that pharmacokinetic-pharmacodynamic studies and early-phase safety studies be performed before proceeding to larger studies of longer duration. In addition, innovative study designs may improve the likelihood of adequately assessing treatment response and mitigating risk before conducting multiyear studies. Learning from past experiences and incorporating this knowledge into new drug development programs will be instrumental in bringing new antiinflammatory therapies to patients.
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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 | 0.306 | 0.214 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.042 | 0.031 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".