A population-based computational approach for arrhythmia prediction and therapy

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, dynamical theories, concepts of evolution, experimental and clinical data to translate computational modeling to clinical medicine. The long-term goal is to generate theoretical results serving as scientific bases for future expensive and time-consuming experimental studies or clinical trials.