Robust W1282X-CFTR rescue by a small molecule GSPT1 degrader
Bibliographic record
Abstract
Abstract With the approval of elexacaftor/tezacaftor/ivacaftor (trade name Trikafta), the vast majority of people with cystic fibrosis (CF) are eligible for CF transmembrane conductance regulator (CFTR) modulator therapy. Remaining individuals have premature termination codons or rare CFTR variants with limited treatment options. Although clinical modulator response can be reliably predicted using primary airway epithelial cells, primary cells carrying rare CFTR variants are scarce. To overcome this obstacle, these cells can be expanded by overexpression of mouse Bmi-1 and human TERT (hTERT). We therefore used this approach to develop two non-CF and three CF (F508del/F508del, F508del/S492F, W1282X/W1282X) nasal cell lines and two W1282X/W1282X bronchial cell lines. Bmi-1/hTERT cell lines recapitulated primary cell morphology and ion transport function. The F508del/F508del and F508del/S492F cell lines robustly responded to Trikafta, which was mirrored in the parent primary cells and the cell donors’ clinical response. CC-90009, a novel cereblon E3 ligase modulator targeting the GSPT1 protein, rescued ~20% of wildtype CFTR function in our panel of W1282X/W1282X cell lines and primary cells. Intriguingly, CC-90009 also diminished epithelial sodium channel function. These studies demonstrate that Bmi-1/hTERT cell lines faithfully mirror primary cell responses to CFTR modulators and illustrate novel therapeutic approaches for the W1282X CFTR variant.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".