Early In Vivo Testing to Assess New Therapeutic Interventions in CF Patients
Bibliographic record
Abstract
New therapeutic strategies are targeting correction of the basic defect in cystic fibrosis (CF) disease. In fact, completion of the first successful clinical drug trials now signals the start of a new era in CF therapy. Many promising drug candidates are emerging into the clinical drug pipeline. However, their translation from the bench to the bed side is challenged by the lack of accurate and reliable biomarker assays that allow testing for their clinical efficiency and safety in early clinical trials. It is surprising that despite the availability of modern equipment and technologies relatively little effort has been directed towards innovative approaches to exploit our pathophysiological understanding of CF disease for the design of novel assays that allow in vivo assessment of CFTR dysfunction as the measurable correlate of the basic defect of CF disease. This lack of adequate outcome measure is now gaining increased attention, and first studies are being initiated to screen larger CF patient cohorts for biological markers that can be used as a potential measure of drug response. This paper reviews currently available in vivo tests, highlighting new methods and their potential use as early in vivo markers for therapeutic investigations. Finally, key criteria of the validation process that needs to be addressed before new biomarker assays can be introduced into clinical trials are discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".