<i>In silico</i>prediction of drug therapy in catecholaminergic polymorphic ventricular tachycardia
Bibliographic record
Abstract
KEY POINTS: The mechanism of therapeutic efficacy of flecainide for catecholaminergic polymorphic ventricular tachycardia (CPVT) is unclear. Model predictions suggest that Na(+) channel effects are insufficient to explain flecainide efficacy in CPVT. This study represents a first step toward predicting therapeutic mechanisms of drug efficacy in the setting of CPVT and then using these mechanisms to guide modelling and simulation to predict alternative drug therapies. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmia syndrome characterized by fatal ventricular arrhythmias in structurally normal hearts during β-adrenergic stimulation. Current treatment strategies include β-blockade, flecainide and ICD implementation--none of which is fully effective and each comes with associated risk. Recently, flecainide has gained considerable interest in CPVT treatment, but its mechanism of action for therapeutic efficacy is unclear. In this study, we performed in silico mutagenesis to construct a CPVT model and then used a computational modelling and simulation approach to make predictions of drug mechanisms and efficacy in the setting of CPVT. Experiments were carried out to validate model results. Our simulations revealed that Na(+) channel effects are insufficient to explain flecainide efficacy in CPVT. The pure Na(+) channel blocker lidocaine and the antianginal ranolazine were additionally tested and also found to be ineffective. When we tested lower dose combination therapy with flecainide, β-blockade and CaMKII inhibition, our model predicted superior therapeutic efficacy than with flecainide monotherapy. Simulations indicate a polytherapeutic approach may mitigate side-effects and proarrhythmic potential plaguing CPVT pharmacological management today. Importantly, our prediction of a novel polytherapy for CPVT was confirmed experimentally. Our simulations suggest that flecainide therapeutic efficacy in CPVT is unlikely to derive from primary interactions with the Na(+) channel, and benefit may be gained from an alternative multi-drug regimen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".