A long sought goal has been to develop drugs to manage cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered. A primary reason that pharmacological management of cardiac arrhythmia has failed is because there is currently no way to predict how ion channel blocking drugs with intrinsically complex properties, active metabolites and off-target effects will alter emergent electrical behavior generated in the heart. New approaches that provide a predictive link between the processes of complex drug interactions at the subcellular scale and the resultant emergent effects on organ level electrical behavior are desperately needed. In order to begin to bridge the gap, we have brought together an expert team to assemble and test a new multiscale model framework that connects for the first time detailed mathematical models to predict atomic scale interactions of drugs and ion channels to functional scale predictions at the level of the channel, cell, tissue and organ. An unprecedented link will be formed as we plan to use predictions from the atomic structure simulations to inform the kinetic parameters of models that capture the complex dynamical interactions of drugs and ion channels. The computational components will then be studied in predictive models at the cell, tissue and organ scales to expose fundamental mechanisms and complex interactions underlying emergent behaviors. Experiments in human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) and mammalian cells, tissues and organs will be undertaken to validate model predictions in cells and tissue. Drug properties will then be perturbed in models to identify changes to drug properties that improve therapeutic potential. These data will be fed back to predictive simulations and structure models to identify small molecules analogs with predicted requisite function for improved therapy. The multiscale model for prediction of pharmacology that we will develop in this application will be applied to projects demonstrating its usefulness for 1) drug prediction, 2) drug screening and 3) drug therapy. The eventual goal is a scalable, automated platform that will interact with other cutting edge technologies to serve purposes in governmental regulation, industry, academia and in clinical medicine that can expand to predict pharmacology of other common cardiac diseases and disorders of excitability such as epilepsy, ataxia and pain.
Disorders of excitability kill more Americans than any other cause and are difficult to treat with drugs. There is an urgent need to develop new approaches for predicting how drugs will affect cardiac rhythms. Our team will construct a novel computational multiscale model framework for predicting drug effects on emergent electrical activity in the heart.
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