Nasal cells as a bronchial cell surrogate for pre-clinical assessment of drug response in cystic fibrosis
Bibliographic record
Abstract
Patient-derived airway cell cultures are used in personalized medicine strategies for people with cystic fibrosis (pwCF) to predict potential clinical response to cystic fibrosis transmembrane conductance regulator (CFTR) modulator drugs. While bronchial epithelial cells from lung explants (HBEx) are the gold standard for CFTR functional measurements, nasal epithelial cells (HNE) are a more practical tissue source resulting in widespread use for preclinical functional platforms. HNE have so far not been rigorously validated against the gold standard for this purpose. In this study, we collected nasal and bronchial cells, and lung explants from pwCF undergoing lung transplantation as well as non-CF controls. Comparative studies in non-CF cells showed that while CFTR-mediated transepithelial currents in HNE underestimated those in HBEx, the magnitude of the CFTR modulator response was similar between CF HNE, brushed HBE (HBEb), and HBEx with significant correlation between matched HNE and HBEb from 16 pwCF. These findings confirm use of HNE as surrogate of bronchial airway for preclinical drug testing with report of drug responses in relation to the tissue-specific non-CF or baseline controls rather than as absolute results. Furthermore, CF centres offering HNE-based drug testing utilize different techniques, challenging the comparison of results between centres. We show how culture media, use of fresh or freeze-thawed cells as well as difference in Ussing technique impact the magnitude of measured CFTR function, which is why we suggest diligence in reporting of these factors when presenting CFTR modulator drug response results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".