Pore- and voltage sensor–targeted KCNQ openers have distinct state-dependent actions
Bibliographic record
Abstract
conductance in the central nervous system, referred to as the M current, which is controlled by membrane voltage and PIP2. The KCNQ2-5 voltage-gated potassium channels are targeted by a variety of activating compounds that cause negative shifts in the voltage dependence of activation. The underlying pharmacology of these effects is of growing interest because of possible clinical applications. Recent studies have revealed multiple binding sites and mechanisms of action of KCNQ activators. For example, retigabine targets the pore domain, but several compounds have been shown to influence the voltage-sensing domain. An important unexplored feature of these compounds is the influence of channel gating on drug binding or effects. In the present study, we compare the state-dependent actions of retigabine and ICA-069673 (ICA73, a voltage sensor-targeted activator). We assess drug binding to preopen states by applying drugs to homomeric KCNQ2 channels at different holding voltages, demonstrating little or no association of ICA73 with resting states. Using rapid solution switching, we also demonstrate that the rate of onset of ICA73 correlates with the voltage dependence of channel activation. Retigabine actions differ significantly, with prominent drug effects seen at very negative holding voltages and distinct voltage dependences of drug binding versus channel activation. Using similar approaches, we investigate the mechanistic basis for attenuation of ICA73 actions by the voltage-sensing domain mutation KCNQ2[A181P]. Our findings demonstrate different state-dependent actions of pore- versus voltage sensor-targeted KCNQ channel activators, which highlight that subtypes of this drug class operate with distinct mechanisms.
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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) | 0.002 | 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".