Insights into the Molecular Mechanism of hERG1 Channel Activation and Blockade by Drugs
Bibliographic record
Abstract
Blockade of the human ether-a-go-go related gene 1 (hERG1) channel has been associated with an increased duration of ventricular repolarization, causing prolongation of the time interval between Q and T waves (long QT syndrome, or LQTS). LQTS may result in serious cardiovascular disorders such as tachyarrhythmia and sudden cardiac death. Diverse types of organic compounds bind to the wide intracellular cavity in the pore domain of hERG channels, leading to a full or partial blockade of ion current through the pore. The drug– induced blockade of the hERG-related component of the potassium current is thought to be a major reason for drug– induced arrhythmias in humans. Identification of specific interactions governing the high-affinity blockade of cardiac potassium (K–) channels is crucial both for the prevention of unintended ion channel block and for the design of ion channel modulators. A plethora of ligand- and receptor-based models of K-channels have been created to address these challenges. In this paper, we review the current state of knowledge regarding the structure-function relationship of hERG and discuss progress in the use of molecular modeling for developing both blockers and activators of hERG. Keywords: hERG, biological channels, long QT syndrome, channel activation and blockade, blockers and activators of hERG, hERG screening, potent modeling strategies of ion channels, hERG1, Drugs, ventricular repolarization, ligand- and receptor-based models, structure-function relationship, potent modeling, strategies of ion channels, transmembrane, anti-arrhythmic activity, proarrhythmic-drug-induced LQTS, non-cardiac medications, antibiotics, antihistamines, antibacterials, terfenadine, cisapride, astemizole, grepafloxin
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".