Insights into the Interactions between CFTR and Small Molecule Modulators
Bibliographic record
Abstract
Cystic Fibrosis (CF) is a life-threatening autosomal recessive disease affecting 1:3600 children born in Canada. CF is caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) chloride channel. The most common disease causing mutation is a deletion of residue F508, resulting in a structurally compromised protein which is retained in the endoplasmic reticulum and targeted for proteasomal degradation. Therapeutic strategies currently being pursued to alleviate the afflictions caused by this and other mutants include the use of corrector compounds to modify the surface expression of the channel, and potentiator compounds to increase cAMP-mediated channel activity. Despite the discovery of a number of small molecules affecting CFTR, much is still unknown about the nature of these interactions. This thesis contains the investigation of two potentiators: VRT-532 and VX-770, and two correctors VX-809 and C18. We assessed the consequences of interactions with these drugs on CFTR channel activity, ATPase activity and phosphorylation. We demonstrated that VRT-532 binds directly to mutant CFTR to modify its channel and ATPase activity. VX-770, known to bind directly to CFTR, can stimulate channel activity in the absence of cAMP stimulation in baby hamster kidney (BHK) cells. Correctors VX-809 and C18, based off the same molecular scaffold, are both capable of acutely augmenting cAMP-stimulated channel activity, providing evidence for potentiator activities in these compounds. Quantitative mass spectrometry (MS) techniques demonstrate a defect in phosphorylation at Ser-660 in the regulatory (R) domain in the major mutant. Treatment with C18 was unable to repair this defect. These novel findings regarding interactions between several small molecules and CFTR contributes to the understanding of the mechanism of action of these compounds, and will help identify how they may be modified for greater efficacy to improve the treatment of CF disease.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".