# A population-based computational approach for arrhythmia prediction and therapy

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $393,750

## Abstract

PROJECT SUMMARY
 Sudden cardiac death is primarily caused by ventricular arrhythmias, accounting for nearly half of all
cardiovascular disease deaths in the US. However, most of the currently available antiarrhythmic drugs are
proarrhythmic, and while implanted cardioverter defibrillators are believed to be the most effective therapy, their
low efficacy and high cost pose significant challenges. Both implanted cardioverter defibrillators and drug
therapies necessitate accurate risk stratification, and drug therapies require not only a comprehensive
understanding of the mechanisms but also the identification of appropriate drug targets. The difficulties in risk
stratification and identifying the right drug targets are that: 1) at the individual scale, arrhythmias have multiple
and multiscale causes and mechanisms. Drugs target entities at the molecular scale but arrhythmias are
fundamentally tissue-scale phenomena, with no simple one-to-one relationships due to complex multiscale
nonlinear interactions. An antiarrhythmic drug may suppress one particular arrhythmia mechanism but potentiate
another mechanism, unexpectedly increasing rather than decreasing mortality as shown in large clinical trials;
and 2) at the population scale, a drug may be antiarrhythmic for one individual but proarrhythmic for another due
to inter-individual variability/diversity and complex environmental differences, which may also account for the
failure of current antiarrhythmic drug therapies. Therefore, for antiarrhythmic drug discovery, one must evaluate
the effects of a molecular intervention or a drug on not just a single arrhythmia mechanism, but all possible
arrhythmia mechanisms. Additionally, one must take into account inter-individual variability and complex
environmental stresses. Our goal is to use mathematical modeling, computer simulation, dynamical theories,
and "virtual clinical trials" in our in silico platform that includes normal and diseased human model populations,
and leverage the power of computer modeling and simulation in dealing with complexity to discover novel
effective antiarrhythmic drug targets for arrhythmia prevention and novel ECG markers for risk prediction. Our
central hypothesis is that dynamical instabilities are the major common mechanisms of arrhythmogenesis
regardless of the underlying biological causes, and suppressing dynamical instabilities by targeting the
appropriate dynamical parameters can be effective unified therapies for arrhythmia prevention. There are two
specific aims: 1) To discover effective antiarrhythmic drug therapies and test the hypothesis that targeting certain
dynamical parameters can be effective unified therapeutic targets; 2) To discover optimal clinical markers for
arrhythmia risk prediction and test the hypothesis that dynamically-sensitive ECG properties can be effective risk
predictors. This is a both data-driven and hypothesis-driven proposal which integrates computational modeling
and simulation, dynami...

## Key facts

- **NIH application ID:** 10943723
- **Project number:** 1R01HL175074-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** ZHILIN QU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $393,750
- **Award type:** 1
- **Project period:** 2024-07-09 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943723

## Citation

> US National Institutes of Health, RePORTER application 10943723, A population-based computational approach for arrhythmia prediction and therapy (1R01HL175074-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10943723. Licensed CC0.

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