State‐dependent regulation of the K <sub>v</sub> 7.2 channel voltage sensor by QO‐58
Bibliographic record
Abstract
Background and Purpose K V 7(KCNQ) potassium channels are key modulators of neuronal excitability, and promising targets for development of drugs for epilepsy and pain. We investigated a candidate K V 7 drug, QO‐58, which has been previously described but has an unclear mechanism of action. Experimental Approach Targeted mutations or chimeric rearrangements of K V 7 channels were used to investigate potential sites of drug binding. K V 7 channels were expressed in Xenopus oocytes or HEK cells for electrophysiology and pharmacological characterisation. Key Results QO‐58 shares characteristic features of other voltage sensor domain (VSD)‐targeted potentiators. These include subtype specificity for K V 7.2 over K V 7.3, prominent state‐dependent actions, and marked deceleration of closure of K V 7.2. VSD mutations influence the actions of QO‐58 and another VSD‐targeted drug, ICA‐069673. Interestingly, K V 7.2[F168L] mutation, previously shown to abolish ICA‐069673 sensitivity, does not weaken QO‐58 actions. However, K V 7.2[F168W] reduces QO‐58 effects, while preserving sensitivity to ICA‐069673. This finding indicates subtle differences in the interaction of these drugs with the VSD binding site. Conclusions and Implications Key contacts underlying sensitivity to VSD‐targeted potentiators may vary significantly depending on the chemical and steric features of the drug. We anticipate that further investigation of VSD‐targeted drugs will continue to clarify the complex pharmacophore of the VSD binding pocket in K V 7 channels.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".