Targeting the E1 ubiquitin-activating enzyme (UBA1) improves elexacaftor/tezacaftor/ivacaftor efficacy towards F508del and rare misfolded CFTR mutants
Bibliographic record
Abstract
The advent of Trikafta (Kaftrio in Europe) (a triple-combination therapy based on two correctors-elexacaftor/tezacaftor-and the potentiator ivacaftor) has represented a revolution for the treatment of patients with cystic fibrosis (CF) carrying the most common misfolding mutation, F508del-CFTR. This therapy has proved to be of great efficacy in people homozygous for F508del-CFTR and is also useful in individuals with a single F508del allele. Nevertheless, the efficacy of this therapy needs to be improved, especially in light of the extent of its use in patients with rare class II CFTR mutations. Using CFBE41o- cells expressing F508del-CFTR, we provide mechanistic evidence that targeting the E1 ubiquitin-activating enzyme (UBA1) by TAK-243, a small molecule in clinical trials for other diseases, boosts the rescue of F508del-CFTR induced by CFTR correctors. Moreover, TAK-243 significantly increases the F508del-CFTR short-circuit current induced by elexacaftor/tezacaftor/ivacaftor in differentiated human primary airway epithelial cells, a gold standard for the pre-clinical evaluation of patients' responsiveness to pharmacological treatments. This new combinatory approach also leads to an improvement in CFTR conductance on cells expressing other rare CF-causing mutations, including N1303K, for which Trikafta is not approved. These findings show that Trikafta therapy can be improved by the addition of a drug targeting the misfolding detection machinery at the beginning of the ubiquitination cascade and may pave the way for an extension of Trikafta to low/non-responding rare misfolded CFTR mutants.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".