| A long sought, and thus far elusive, goal has been to develop drugs to manage diseases of excitability. One such disease affecting millions each year is cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered. A major reason that pharmacological management of cardiac arrhythmia has failed is because there is no adequate framework currently available to predict how drugs that target cardiac ion channels, and that have intrinsically complex dynamics, will alter the emergent electrical behavior generated in the heart.;In Part I of this thesis, I present a computational modeling approach, based on and validated by experimental data, that defines key measurable parameters necessary to accurately simulate the kinetics of antiarrhythmic drugs with cardiac Na+ channels, and then predicts their effects on simulated human cardiac rhythms. The model forecasts the timing of specific activation sequences where clinically relevant concentrations of the antiarrhythmic drugs flecainide and lidocaine will cause, rather than prevent, arrhythmia. We then conducted experiments in rabbit hearts to validate the model predictions. Simulations in virtual human cardiac tissue suggest a "safe concentration range" for therapeutic use.;In Part II of this thesis, I extend our analysis to a congenital disease of cardiac arrhythmia -- the long QT3 syndrome (LQT3) -- and study a particular variant, the DeltaKPQ mutation. I model the effects of a new drug, ranolazine, which specifically targets the mutation-induced late Na+ current, on the AKPQ mutant heart, and assess antiarrhythmic efficacy by simulating pacing sequences common in these patients.;The model framework presented in this thesis is the foundation for development of a high-throughput virtual drug testing system that will allow for integration of data on drug / channel interactions, more accurate prediction of treatment efficacy that is genotype specific, and prediction of drug effects on emergent dynamics in a complex excitable system. |
