CFTR modulator theratyping: Current status, gaps and future directions
Bibliographic record
Abstract
BACKGROUND: New drugs that improve the function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein with discreet disease-causing variants have been successfully developed for cystic fibrosis (CF) patients. Preclinical model systems have played a critical role in this process, and have the potential to inform researchers and CF healthcare providers regarding the nature of defects in rare CFTR variants, and to potentially support use of modulator therapies in new populations. METHODS: The Cystic Fibrosis Foundation (CFF) assembled a workshop of international experts to discuss the use of preclinical model systems to examine the nature of CF-causing variants in CFTR and the role of in vitro CFTR modulator testing to inform in vivo modulator use. The theme of the workshop was centered on CFTR theratyping, a term that encompasses the use of CFTR modulators to define defects in CFTR in vitro, with application to both common and rare CFTR variants. RESULTS: Several preclinical model systems were identified in various stages of maturity, ranging from the expression of CFTR variant cDNA in stable cell lines to examination of cells derived from CF patients, including the gastrointestinal tract, the respiratory tree, and the blood. Common themes included the ongoing need for standardization, validation, and defining the predictive capacity of data derived from model systems to estimate clinical outcomes from modulator-treated CF patients. CONCLUSIONS: CFTR modulator theratyping is a novel and rapidly evolving field that has the potential to identify rare CFTR variants that are responsive to approved drugs or drugs in development.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".