ISCRM Researchers Pursue Personalized Medicine for Heart Failure

Jen Davis and Farid Moussavi-Harami
ISCRM faculty members Jen Davis, PhD and Farid Moussavi Harami, MD

Heart failure is a chronic condition in which the heart is unable to pump adequate amounts of blood to the rest of the body. The disease impacts more than six million adults in the United States – and the incidence is rising. Each year heart failure contributes to nearly 400,000 deaths in America and drains roughly $30 billion from the economy.

While heart failure can be managed, physicians rely on traditional, one-size-fits-all diagnostic methods and treatments. One solution could be broader adoption of precision medicine – an approach to disease treatment in which care plans are based on factors like patient genetics, drug sensitivities and resistances, environment, and lifestyle.

Personalizing Treatment for Heart Failure

Doctors are already using precision medicine strategies to extend the lives of cancer patients. Now, a Collaborative Science Award from the American Heart Association will allow a research team from the University of Washington to develop new tools to help cardiologists personalize treatments for certain heart diseases too.

Tom Daniel
Tom Daniel, PhD, Biology

The collaborative grant, titled Multi-Scale Computational Modeling in Genetic Cardiomyopathies, brings together three complementary areas of expertise: molecular cardiovascular biology, cardiac physiology and muscle mechanics, and computational biology. The research effort will be co-led by Jen Davis PhD, Associate Professor of Lab Medicine and Pathology and Bioengineering; Farid Moussavi-Harami MD, Assistant Professor of Medicine/Cardiology; and Tom Daniel PhD, Professor of Biology.

Davis and Moussavi-Harami are faculty members in the Institute for Stem Cell and Regenerative Medicine (ISCRM). Daniel leads the Quantitative Analysis Core in the UW Center for Translational Muscle Research (CTMR), which is supporting the research effort through a pilot grant.

Muscle Center Diagram
The collaboration between Davis, Moussavi-Harami, and Daniel is also supported by a pilot grant from UW Center for Translational Muscle Research.

“The muscle center was created to facilitate collaborations just like this and to accelerate muscle research efforts with translational potential,” says Mike Regnier, PhD, Professor of Bioengineering and Physiology & Biophysics.

According to Davis, the project aims to apply new knowledge and technologies to the clinic.  “Over the last few years, advances in DNA sequencing have helped the field identify hundreds of new genetic mutations causal for inherited heart disease. Rather than develop a treatment for each one of these mutations, we think there are patterns with predictive value that we can use to cluster the mutations, which in turn could reduce the number of different treatments needed to address each individual mutation.”

Making More Informed Decisions for Optimized Treatment

Moussavi-Harami had collaborated with both Davis and Daniel on previous projects. He says the complementary skill sets of the three investigators add up to exciting possibilities. “Speaking as physician, I see how heart failure affects patients from the cellular level to the whole person. The opportunity to team up with experts like Jen and Tom is definitely a game changer. “If we know the profile of a patient’s disease, we can choose drugs that push the contraction force in the right direction. We can even make more informed decisions about dosage and duration.”

Specifically, Davis and her collaborators believe they can group patient profiles into treatment categories using a key signal from the body itself – the amount of tension a muscle cell produces in the course of a single heartbeat. Indeed, Davis and Moussavi-Harami discovered this signal in a 2016 Cell paper and then recently validated this predictive index in a study published in JCI Insight (2020).

By adapting sophisticated technology developed over several years by the Daniel Lab, the researchers will be able to precisely model the full range of possible cardiac muscle contractions, allowing them to create a data model that will help them benchmark healthy and unhealthy heartbeat patterns.

“We are incredibly excited to bring computational modeling and machine learning to address issues that encompass both skeletal and cardiac myopathies,” says Daniel. ” Through the CTMR, we are able to collaborate with a team of outstanding clinical and basic research scientists.”

A Big Moment for Heart Research

The next challenge is to begin to categorize all the possible genetic mutations that could potentially contribute to irregular cardiac muscle contraction and its influence on maladaptive muscle growth that ultimately results in heart failure. The investigation funded by the AHA grant will focus on hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) – two types of heart disease that are commonly screened using genetic testing.

Computational Model
Simplified scheme of the sarcomere, the muscle contractile unit, with the important components.

Davis explains that a stem cell line created in her lab will help the researchers to measure how well the computational model predicts outcomes. A mouse model will also provide an in vivo representation of the data. Davis adds, “Eventually, our goal is to use stem cells to mutate every gene in a muscle involved in contraction.”

This groundbreaking application of computational modeling, stem cells, and genetics are the keys to grouping the overwhelming numbers of possible mutations into just a few clusters, making diagnosis and treatment much more manageable – and hopefully more effective for an individual patients.

“This is a big moment in time for heart research,” says Moussavi-Harami. “We’re discovering more about how genetics contributions to heart failure and finding real-world applications for powerful tools. It means we now have the potential to help physicians significantly improve patient outcomes. This is what personalized medicine is all about.”

“We want to develop tools that clinicians with minimal computational training can use to predict heart problems in patients,” says Davis. “This would bring us closer to a true precision medicine approach for inherited heart diseases.”