# Personalizing Class I anti-Arrhythmic Drug Therapy

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $670,491

## Abstract

Summary
We want to improve arrhythmia therapy outcomes by predicting an optimal anti-arrhythmic drug
regimen for ventricular arrhythmia patients using genomic data and heart rhythm reports from
wearable and implanted devices. Today, amiodarone is commonly prescribed to prevent painful
shocks from defibrillators that are implanted in patients who suffer from ventricular arrhythmia.
Nevertheless, amiodarone is toxic for many patients when it is used over a period of years. In
contrast, the class 1b molecule, mexiletine has many fewer side effects, but is effective in fewer
patients. We have shown that cardiac Na+ channel variants in patients with Long QT Type 3
Syndrome can significantly affect key biophysical gating parameters that determine whether
patients respond to mexiletine therapy. We propose to apply a similar approach to determine
how common genetic variants and β-adrenergic tone regulate mexiletine response in patients
with ventricular arrhythmias. The project aims have been developed to 1) gain novel insight into
the molecular mechanisms of β-adrenergic and variant regulation of the drug response, 2)
create a predictive model by mapping parameters associated with these mechanisms to patient
response in a clinical study, and 3) improve the model by gaining insight from an optimized
induced pluripotent stem cell based model of the drug response. If the aims of the proposal are
successful, we will set the stage for a prospective clinical trial that uses a predictive model to
identify ventricular arrhythmia patients who will respond to mexiletine based on data that is
becoming readily available to physicians.

## Key facts

- **NIH application ID:** 10397473
- **Project number:** 5R01HL148803-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** JONATHAN R SILVA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $670,491
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10397473

## Citation

> US National Institutes of Health, RePORTER application 10397473, Personalizing Class I anti-Arrhythmic Drug Therapy (5R01HL148803-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10397473. Licensed CC0.

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