Investigating the utility of adult zebrafish ex vivo whole hearts to pharmacologically screen hERG channel activator compounds
Bibliographic record
Abstract
There is significant interest in the potential utility of small-molecule activator compounds to mitigate cardiac arrhythmia caused by loss of function of hERG1a voltage-gated potassium channels. Zebrafish ( Danio rerio) have been proposed as a cost-effective, high-throughput drug-screening model to identify compounds that cause hERG1a dysfunction. However, there are no reports on the effects of hERG1a activator compounds in zebrafish and consequently on the utility of the model to screen for potential gain-of-function therapeutics. Here, we examined the effects of hERG1a blocker and types 1 and 2 activator compounds on isolated zkcnh6a (zERG3) channels in the Xenopus oocyte expression system as well as action potentials recorded from ex vivo adult zebrafish whole hearts using optical mapping. Our functional data from isolated zkcnh6a channels show that under the conditions tested, these channels are blocked by hERG1a channel blockers (dofetilide and terfenadine), and activated by type 1 (RPR260243) and type 2 (NS1643, PD-118057) hERG1a activators with higher affinity than hKCNH2a channels (except NS1643), with differences accounted for by different biophysical properties in the two channels. In ex vivo zebrafish whole hearts, two of the three hERG1a activators examined caused abbreviation of the action potential duration (APD), whereas hERG1a blockers caused APD prolongation. These data represent, to our knowledge, the first pharmacological characterization of isolated zkcnh6a channels and the first assessment of hERG enhancing therapeutics in zebrafish. Our findings lead us to suggest that the zebrafish ex vivo whole heart model serves as a valuable tool in the screening of hKCNH2a blocker and activator compounds.
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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".