Torsades de Pointes, a lethal ventricular arrhythmia, is a side effect of several commonly used antiarrhythmics, antibiotics, antipsychotics, antihistamines and other ?non-cardiovascular? therapies. Though this adverse event is rare, it can lead to ventricular fibrillation and sudden cardiac death. The ignorance about the underlying differences between those at high risk versus low risk of forming this drug-induced arrhythmia halts any considerable progress in preventing it. Rather than simply removing these drugs from the market, a closer examination of the physiological and clinical traits of patients who benefited from the treatment and those who formed the arrhythmia needs to be performed. This highlights the idea of precision medicine and the importance of identifying relevant sub-groups of patients likely to benefit from a treatment versus those who are highly susceptible to a drug-induced adverse event. The current standards for predicting risk, a lengthened action potential (AP) duration of cells and a prolonged QT interval on an echocardiogram (ECG) have proven ineffective. Thus, there is a need to extract pertinent information from the cellular and tissue levels before administration of the therapeutic to detect patterns only apparent in the high-risk population. To analyze this concept, I plan to (1) explain at a mechanistic level the differences between the healthy and at-risk patients, (2) identify important AP and ECG signatures that can predict risk early on, and (3) connect the physiological and clinical findings to improve the profile and description of the high-risk population. I will combine two complementary computational techniques: (1) simulations with mechanistic quantitative systems pharmacology models of heart cells and tissues; and (2) advanced machine learning approaches that can identify hidden patterns. Thus, this project aims to develop an algorithm which will improve risk prediction and upgrade the current imperfect and unreliable standards for prescribing proarrhythmic therapies.

Public Health Relevance

Current standards for identifying patients at risk of forming the rare but lethal ventricular tachycardia, Drug- induced Torsades de Pointes include measuring the QT interval on the electrocardiogram (ECG). Although this decently predicts susceptibility, there remains a percentage of patients who still succumb to an arrhythmia with a normal QT. To improve patient risk stratification, this research project aims to develop an algorithm that can unmask subtle precursors at both the physiological and clinical levels, classify high risk patients, and upgrade the current imperfect standards for prescribing these therapies.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
5F31HL149358-02
Application #
10082298
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lidman, Karin Fredriksson
Project Start
2019-12-01
Project End
2023-02-20
Budget Start
2021-02-21
Budget End
2022-02-20
Support Year
2
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Pharmacology
Type
Schools of Medicine
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029