Personalized medicine involves developing treatments targeted to patients who may not respond to the standard of care. A biomedical engineer in the McKelvey School of Engineering at Washington University in St. Louis is using genetics, biophysics and predictive simulation to study a potential personalized treatment for patients with arrhythmia that has fewer side effects than the standard treatment for the condition.
Jonathan Silva, associate professor of biomedical engineering, will study the efficacy of the drug mexiletine on patients with arrhythmia with a five-year, $3.17 million grant from the National Institutes of Health's National Heart, Lung, and Blood Institute. The grant builds on years of work Silva has done both computationally in his lab and experimentally with collaborators.
The standard of care for patients with ventricular arrhythmia is an implanted cardiac defibrillator and treatment with the drug amiodarone, which works well on many patients but can have toxic side effects such as thyroid dysfunction, pulmonary and liver toxicity and glaucoma with long-term use. Another drug, mexiletine, is designed to block extra current that travels through the sodium channel in the heart and causes fatal heart rhythms. However, the drug is not effective for every patient, though researchers are not clear why. Silva has been seeking answers, most recently developing the first computational model that showed the molecular groundwork for mexiletine's effectiveness in some patients but not others.
In collaboration with Nathaniel Huebsch, assistant professor, and Rohit Pappu, the Edwin H. Murty Professor of Engineering, both in biomedical engineering in the McKelvey School of Engineering; Jonathan Moreno, MD, PhD, a postdoctoral researcher, and Jennifer Silva, MD, associate professor of pediatrics and director of pediatric electrophysiology, both at Washington University School of Medicine; and Céline Marionneau of the University of Nantes, France; Jonathan Silva will conduct a clinical study with both adult and pediatric patients with arrhythmia to monitor symptoms and defibrillator activity while taking mexiletine to inform a mechanistically-based predictive model.
"Most physicians aren't able to test each potential drug in patients, so they prescribe the ones that work on the most people," Jonathan Silva said. "Our argument is what if we can start predicting which patients would respond to which drug so that physicians can prescribe the one with the most benefit and that is the least harmful?"
Patients in the study will get baseline testing at the beginning of the study, and then will be monitored after 6 months and 12 months. Silva, Moreno and Silva will map the biophysical parameters, found in collaboration with Pappu and Marionneau, for each patient to build a new computational model that predicts their response to the drug.
In addition, in patients who do not respond as predicted, Jonathan Silva and Huebsch will characterize their pluripotent stem cells — stem cells taken from blood that can be made into any other kind of cell — that will be reprogrammed into cardiac muscle cells, or cardiomyocytes.
"This will allow us to identify novel regulators of mexiletine response that can be used to improve the predictive model," Jonathan Silva said.
In 2018, Silva published results of a small study of eight patients with Long QT Syndrome Type 3 in whom they had predicted a response to mexiletine based on his computational model. His team's predictions were correct in seven of the eight patients.
Jonathan Silva said this study has the potential to impact personalized medicine more broadly
"In the future, maybe everyone has their genome in their medical record, so when the physician pulls it up to prescribe an anti-arrhythmic drug, there would be a note that predicts whether or not the patient will respond well to mexiletine and forego the harmful trial-and-error approach of today," he said.
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