Investigating the state dependence of drug binding in hERG channels using a trapped-open channel phenotype
Bibliographic record
Abstract
The hERG channel is a key player in repolarization of the cardiac action potential. Pharmacological blockade of hERG channels depletes the cardiac repolarization reserve, increasing the risk of cardiac arrhythmias. The promiscuous nature of drug interactions with hERG presents a therapeutic challenge for drug design and development. Despite considerable effort, the mechanisms of drug binding remain incompletely understood. One proposed mechanism is that high-affinity drug binding preferentially occurs when channels are in the inactivated state. However, this has been difficult to test, since inactivation is rapid in hERG and access to the drug binding site is limited by slower opening of the activation gate. Here, we have directly assessed the role of inactivation in cisparide and terfenadine drug binding in mutant (I663P) hERG channels where the activation gate is trapped-open. We firstly demonstrate the utility of this approach by showing that inactivation, ion selectivity and high affinity drug binding are preserved in I663P mutant channels. We then assess the role of inactivation by applying cisapride and terfenadine at different membrane voltages, which induce varying degrees of inactivation. We show that the extent of block does not correlate with the extent of inactivation. These data suggest that inactivation is not a major determinant of cisapride or terfenadine binding in hERG 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 | 0.001 |
| 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".