Clinical development of triple-combination CFTR modulators for cystic fibrosis patients with one or two<i>F508del</i>alleles
Bibliographic record
Abstract
Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator gene (CFTR) that result in diminished quantity and/or function of the CFTR anion channel.F508del-CFTR, the most common CF-causing mutation (found in ∼90% of patients), causes severe processing and trafficking defects, resulting in decreased CFTR quantity and function. CFTR modulators are medications that increase the amount of mature CFTR protein (correctors) or enhance channel function (potentiators) at the cell surface. Combinations of CFTR correctors and potentiators (i.e.lumacaftor/ivacaftor, tezacaftor/ivacaftor) have demonstrated clinical benefit in subsets of patients. However, none are approved for patients with CF heterozygous forF508del-CFTR and a minimal function mutation,i.e.a mutation that produces either no protein or protein that is unresponsive to currently approved CFTR modulators. Next-generation CFTR correctors VX-659 and VX-445, each in triple combination with tezacaftor and ivacaftor, improve CFTR processing, trafficking and functionin vitroand have demonstrated clinical improvements in phase 2 studies in patients with CF with one or twoF508del-CFTRalleles. Here, we present the rationale and design of four randomised phase 3 studies, and their open-label extensions, evaluating VX-659 (ECLIPSE) or VX-445 (AURORA) plus tezacaftor and ivacaftor in patients with one or twoF508del-CFTRalleles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".