# Combining Systems Pharmacology Modeling With Machine Learning To Identify Sub-Populations At Risk Of Drug-Induced Torsades de Pointes

> **NIH NIH F31** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $34,031

## Abstract

Project Summary
 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.

## Key facts

- **NIH application ID:** 10082298
- **Project number:** 5F31HL149358-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Meera Varshneya
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $34,031
- **Award type:** 5
- **Project period:** 2019-12-01 → 2021-09-26

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10082298

## Citation

> US National Institutes of Health, RePORTER application 10082298, Combining Systems Pharmacology Modeling With Machine Learning To Identify Sub-Populations At Risk Of Drug-Induced Torsades de Pointes (5F31HL149358-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10082298. Licensed CC0.

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