A Precision Medicine Approach to Optimize Modulator Therapy for Rare CFTR Folding Mutants
Bibliographic record
Abstract
Trikafta, a triple-combination drug, consisting of folding correctors VX-661 (tezacaftor), VX-445 (elexacaftor) and the gating potentiator VX-770 (ivacaftor) provided unprecedented clinical benefits for patients with the most common cystic fibrosis (CF) mutation, F508del. Trikafta indications were recently expanded to additional 177 mutations in the CF transmembrane conductance regulator (CFTR). To minimize life-long pharmacological and financial burden of drug administration, if possible, we determined the necessary and sufficient modulator combination that can achieve maximal benefit in preclinical setting for selected mutants. To this end, the biochemical and functional rescue of single corrector-responsive rare mutants were investigated in a bronchial epithelial cell line and patient-derived human primary nasal epithelia (HNE), respectively. The plasma membrane density of P67L-, L206W- or S549R-CFTR corrected by VX-661 or other type I correctors was moderately increased by VX-445. Short-circuit current measurements of HNE, however, uncovered that correction comparable to Trikafta was achieved for S549R-CFTR by VX-661 + VX-770 and for P67L- and L206W-CFTR by the VX-661 + VX-445 combination. Thus, introduction of a third modulator may not provide additional benefit for patients with a subset of rare CFTR missense mutations. These results also underscore that HNE, as a precision medicine model, enable the optimization of mutation-specific modulator combinations to maximize their efficacy and minimize life-long drug exposure of CF patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".