Long QT syndrome is the most common cardiac arrhythmic disorder, predisposing to sudden cardiac death. There is tremendous genetic heterogeneity leading to long QT syndrome, which leads to considerable variations in disease severity and clinical course. Drug treatments are often ineffective, producing adverse effects in certain populations and prediction of the risk of sudden cardiac death remains fairly primitive. The recent advent of new technological breakthroughs, such as induced pluripotent stem cells (iPSCs), provides an unprecedented opportunity to study associations between genetic variability, drug responsiveness, and disease susceptibility. In addition, the biology of long QT-induced arrhythmia is largely quantifiable and thus amendable to a systems biology approach. The overarching goal of our Systems Biology Collaborative R01 Proposal is to develop an integrative experimental and computational approach to predict patient specific drug responses. To this end, we propose to utilize experimental data from patient-specific iPSC-derived cardiomyocytes (iPSC-CMs) in conjunction with clinical and genomic data, to construct the first, to our knowledge, patient-specific computational model of cardiac electrical activity. We hypothesize that we can use this model to improve our capability to predict arrhythmia susceptibility based on patient genotype as well as drug-response phenotypes associated with genetic variations in silico. We have assembled a team of highly accomplished researchers in cardiac stem cell biology, genomics, pharmacogenomics, molecular genetics/epigenetics, bioinformatics, and in silico modeling. We are well positioned to achieve the project goals within five years. The ability to predict QT response of an individual patient based on their genetic profile would be a novel personalized approach to better understand the mechanisms underlying sudden cardiac death that could ultimately revolutionize treatment strategies.
The emergence of systems biology, which synergistically combines experimental and bioinformatics approaches, offers an analytical framework to capture the molecular complexity of human cardiac disease while offering computational models to predict individual disease manifestations and design targeted therapies. In this proposal, we outline a detailed multidisciplinary research strategy that combines experimental results from patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), clinical, genomic, pharmacogenomics, and in silico modeling data in order to construct a novel patient-specific computational model of cardiac electrophysiology. This new integrated in vitro and in silico paradigm will enable the assessment of patient proarrhythmic risk and could be used to decipher mechanisms of disease and to devise tailored therapeutic therapies for sudden cardiac death.
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