Orkambi® and amplifier co‐therapy improves function from a rare CFTR mutation in gene‐edited cells and patient tissue
Bibliographic record
Abstract
Abstract The combination therapy of lumacaftor and ivacaftor (Orkambi ® ) is approved for patients bearing the major cystic fibrosis (CF) mutation: ΔF508 . It has been predicted that Orkambi ® could treat patients with rarer mutations of similar “theratype”; however, a standardized approach confirming efficacy in these cohorts has not been reported. Here, we demonstrate that patients bearing the rare mutation: c.3700 A>G, causing protein misprocessing and altered channel function—similar to ΔF508‐CFTR, are unlikely to yield a robust Orkambi ® response. While in silico and biochemical studies confirmed that this mutation could be corrected and potentiated by lumacaftor and ivacaftor, respectively, this combination led to a minor in vitro response in patient‐derived tissue. A CRISPR/Cas9‐edited bronchial epithelial cell line bearing this mutation enabled studies showing that an “amplifier” compound, effective in increasing the levels of immature CFTR protein, augmented the Orkambi ® response. Importantly, this “amplifier” effect was recapitulated in patient‐derived nasal cultures—providing the first evidence for its efficacy in augmenting Orkambi ® in tissues harboring a rare CF‐causing mutation. We propose that this multi‐disciplinary approach, including creation of CRISPR/Cas9‐edited cells to profile modulators together with validation using primary tissue, will facilitate therapy development for patients with rare CF mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".