Phenotypic profiling of CFTR modulators in patient-derived respiratory epithelia
Bibliographic record
Abstract
Pulmonary disease is the major cause of morbidity and mortality in patients with cystic fibrosis, a disease caused by mutations in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene. Heterogeneity in CFTR genotype-phenotype relationships in affected individuals plus the escalation of drug discovery targeting specific mutations highlights the need to develop robust in vitro platforms with which to stratify therapeutic options using relevant tissue. Toward this goal, we adapted a fluorescence plate reader assay of apical CFTR-mediated chloride conductance to enable profiling of a panel of modulators on primary nasal epithelial cultures derived from patients bearing different CFTR mutations. This platform faithfully recapitulated patient-specific responses previously observed in the "gold-standard" but relatively low-throughput Ussing chamber. Moreover, using this approach, we identified a novel strategy with which to augment the response to an approved drug in specific patients. In proof of concept studies, we also validated the use of this platform in measuring drug responses in lung cultures differentiated from cystic fibrosis iPS cells. Taken together, we show that this medium throughput assay of CFTR activity has the potential to stratify cystic fibrosis patient-specific responses to approved drugs and investigational compounds in vitro in primary and iPS cell-derived airway cultures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".