Optimization of novel compounds using computer-aided drug design for treatment of cardiac arrhythmia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: 7.1 (KCNQ/KCNE1) channels lead to cardiac arrhythmia such as long QT syndrome, characterized by a prolonged QT interval . One strategy to correct the prolonged QT interval is to design molecules that activate KCNQ1/KCNE1 channels and restore the QT interval. However, there are currently no clinically approved KCNQ1/KCNE1 activators. Polyunsaturated fatty acids (PUFAs) have been shown to be potent activators of KCNQ1/KCNE1, increasing KCNQ1/KCNE1 currents and shortening the action potential duration in human cardiomyocytes. However, PUFAs are unspecific and have many targets, including other cardiac ion channels. EXPERIMENTAL APPROACH: In this study, Site Identification by Ligand Competitive Saturation was used in combination with electrophysiology to optimize compounds that bind to the PUFA binding sites, increasing both their potency and site specificity. KEY RESULTS: Two compounds, Compound 1- linoleic acid (LIN) and Compound 2-LIN, exhibited a more potent activation effect on KCNQ1/KCNE1 channels than our previous PUFA analogues, with each compound demonstrating a distinct activation mechanism. CONCLUSION AND IMPLICATIONS: These findings highlight the potential of computer-aided drug design in developing more targeted and effective KCNQ1/KCNE1 activators, paving the way for personalized therapeutic strategies in treating cardiac disorders. Although the small molecule screening identified compounds with favourable interactions at PUFA binding sites, a lipid tail was required for their effect. This strategy of incorporating lipid tails onto small molecules offers a novel approach for targeting the underexplored transmembrane regions of membrane proteins, which could significantly impact drug development for a wide range of therapeutic targets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".