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Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus

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Predicting drug-induced arrhythmias by multiscale modeling.

Francisco Sahli Costabal¹, Jiang Yao², Ellen Kuhl³

¹Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

International Journal for Numerical Methods in Biomedical Engineering

|February 10, 2018

Summary

This study introduces a computational model to rapidly predict drug-induced cardiac toxicity. The model assesses drug effects on heart rhythms, identifying risks like torsades de pointes to improve drug safety evaluations.

Keywords:

arrhythmia cardiac toxicity drugs electrophysiology finite element analysis torsades de pointes

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Area of Science:

Computational Biology
Cardiovascular Pharmacology
Drug Safety Assessment

Background:

Drug development faces challenges with costly and time-consuming cardiac toxicity risk evaluations.
Undesired drug side effects can lead to lethal cardiac arrhythmias, such as torsades de pointes.

Purpose of the Study:

To establish a high-resolution, multiscale computational model for rapid cardiac toxicity assessment of new and existing drugs.
To reveal the mechanisms of drug-induced electrophysiological abnormalities propagation across scales.

Main Methods:

Utilizing drug-specific current block from single-cell electrophysiology as model input.
Generating spatio-temporal activation profiles and electrocardiograms as model output.
Validating the model with known low-risk (ranolazine) and high-risk (quinidine) drugs.

Main Results:

The model accurately predicted a 19.4% QT interval prolongation for ranolazine.
For quinidine, the model predicted a 78.4% QT interval prolongation and torsades de pointes.
Demonstrated propagation of abnormalities from channel block to ventricular tachycardia.

Conclusions:

The computational model offers a rapid and effective method for assessing drug-induced cardiac toxicity.
This tool can aid researchers, regulatory agencies, and pharmaceutical companies in rationalizing safe drug development.
The model has the potential to reduce the time-to-market for new pharmaceutical compounds.