A computational model to predict the effects of class I anti-arrhythmic drugs on ventricular rhythms

Area of Science:

• Cardiovascular Pharmacology
• Computational Biology
• Cardiac Electrophysiology

Background:

• Diseases of excitability, such as cardiac arrhythmia, affect millions and are difficult to manage pharmacologically.
• Current anti-arrhythmic drugs targeting cardiac ion channels have unpredictable effects on heart electrical activity.
• Predicting drug interactions with ion channels is crucial for managing cardiac arrhythmia.

Purpose of the Study:

• To develop and validate a computational model simulating drug-channel interactions for anti-arrhythmic drugs.
• To predict the effects of flecainide and lidocaine on human ventricular electrical activity.
• To establish a framework for a virtual drug-screening system for cardiac drugs.

Main Methods:

• Applied a computational model informed and validated by experimental data.
• Simulated the interaction kinetics of flecainide and lidocaine with cardiac sodium channels.
• Predicted drug effects on normal human ventricular cellular and tissue electrical activity, including spontaneous ventricular ectopy.

Main Results:

• The computational model predicted that flecainide and lidocaine exacerbate, rather than ameliorate, arrhythmia at clinically relevant concentrations.
• Model predictions were validated through experiments in rabbit hearts and simulations in human ventricles using MRI data.
• Identified key parameters for simulating drug-channel interactions and their impact on emergent electrical behavior.

Conclusions:

• A validated computational model can predict the pro-arrhythmic effects of flecainide and lidocaine.
• This framework represents a significant step towards a virtual drug-screening system for cardiac conditions.
• The study highlights the potential of computational modeling to guide the development of safer and more effective anti-arrhythmic drugs.