# A population-based in silico platform for arrhythmia prediction

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $390,000

## Abstract

PROJECT SUMMARY
 Ventricular arrhythmias are the leading cause of sudden cardiac death accounting for ~300,000 deaths per
year in the US alone. However, no pharmacological or biological therapy has yet emerged with comparable
efficacy to the implantable cardioverter-defibrillator. The major hurdles are: 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. An equally important and crucial problem is effective proarrhythmia risk
(cardiotoxicity) screening for drug safety. In the past, ~30% of the drugs removed from the market were due to
their proarrhythmia risk. Owing to the extreme complexity of the problem, computer modeling and simulation will
be required to evaluate a drug's antiarrhythmic and proarrhythmic effects. Recently, the Cardiac Safety Research
Consortium and FDA have recommended computer simulation as a complementary approach for proarrhythmia
drug screening. However, traditional modeling approaches are limited and population-based modeling
approaches are required. Moreover, the model populations need to accurately account for the inter-individual
variability and arrhythmia mechanisms for accurate prediction of a drug's antiarrhythmic and proarrhythmic
effects. This project proposes to develop a novel in silico platform which includes multiscale normal and diseased
model populations emulating the inter-individual variability of human populations. The model populations will be
filtered and validated against clinical data under normal and diseased conditions. “Virtual clinical trials” will be
then performed for antiarrhythmic drug discovery and drug safety screening. The specific aims are: 1) to develop
and validate an in silico platform incorporating model populations that emulate the inter-individual variability of
human populations under normal and disease conditions; 2) to utilize the in silico human model populations as
a platform for novel antiarrhythmic drug discovery and cardiotoxicity screening. This is a data-driven in silico
approach w...

## Key facts

- **NIH application ID:** 10139088
- **Project number:** 5R01HL134709-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** ZHILIN QU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $390,000
- **Award type:** 5
- **Project period:** 2018-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139088

## Citation

> US National Institutes of Health, RePORTER application 10139088, A population-based in silico platform for arrhythmia prediction (5R01HL134709-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10139088. Licensed CC0.

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