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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".