Functional and behavioral signatures of Kv7 activator drug subtypes
Bibliographic record
Abstract
Abstract Objective Voltage‐gated potassium channels of the KCNQ (Kv7) family are targeted by a variety of activator compounds with therapeutic potential for treatment of epilepsy. Exploration of this drug class has revealed a variety of effective compounds with diverse mechanisms. In this study, we aimed to clarify functional criteria for categorization of Kv7 activator compounds, and to compare the effects of prototypical drugs in a zebrafish larvae model. Methods In vitro electrophysiological approaches with recombinant ion channels were used to highlight functional properties important for classification of drug mechanisms. We also benchmarked the effects of representative antiepileptic Kv7 activator drugs using behavioral seizure assays of zebrafish larvae and in vivo Ca 2+ imaging with the ratiometric Ca 2+ sensor CaMPARI. Results Drug effects on channel gating kinetics, and drug sensitivity profiles to diagnostic channel mutations, were used to highlight properties for categorization of Kv7 activator drugs into voltage sensor‐targeted or pore‐targeted subtypes. Quantifying seizures and ratiometric Ca 2+ imaging in freely swimming zebrafish larvae demonstrated that while all Kv7 activators tested lead to suppression of neuronal excitability, pore‐targeted activators (like ML213 and retigabine) strongly suppress seizure behavior, whereas ICA‐069673 triggers a seizure‐like hypermotile behavior. Significance This study suggests criteria to categorize antiepileptic Kv7 activator drugs based on their underlying mechanism. We also establish the use of in vivo CaMPARI as a tool for screening effects of anticonvulsant drugs on neuronal excitability in zebrafish. In summary, despite a shared ability to suppress neuronal excitability, our findings illustrate how mechanistic differences between Kv7 activator subtypes influence their effects on heteromeric channels and lead to vastly different in vivo outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".