Jeffrey Saucerman, PhD, is a biomedical engineer, professor of cardiovascular medicine, and heart disease researcher. Through computational modeling and high-throughput experiments, he and his team seek to find molecular networks and drugs that can control cardiac remodeling.
In the lab, they've helped develop computational approaches that include large-scale modeling of signaling/gene regulatory networks, machine learning focused on genomic data, and mining of electronic health records.
Here, Saucerman discusses how his cutting-edge techniques can help expand the possibilities of future heart disease care.
What are you working on right now?
We are developing new AI tools to discover drugs that may have the potential to treat heart disease.
What are the most intriguing potential clinical applications of your work?
We have found that the antidepressant Lexapro, prescribed to 30 million Americans, may have an additional benefit for the heart. This finding was produced by our AI tool LogiRx, which may help identify other drugs and be used for other conditions as well.
So far, the predictions for LogiRx have been tested in a retrospective analysis of clinical outcomes from an FDA database and UVA Health. Future clinical trials would be needed before Lexapro could be considered for use in patients to treat the heart.
What made you choose UVA Health as the place to do your research?
UVA Health has a wonderful colocation and collegial atmosphere across biomedical engineering, the biomedical sciences, and the clinic. Collaboration across all three groups has accelerated our ability to translate from computational predictions to analysis of drug outcomes from UVA Health patients.
What do you wish more people knew about your area of research?
Most people know of AI as a science-fiction villain or an unreliable chatbot. But there’s been a lot of progress in more focused AI technologies, such as our LogiRx tool, that are accelerating medical treatments and diagnostics.
How did you become interested in your area of research?
I went to college for computational and mostly nonbiological engineering. But I took a couple of classes in cell biology, in which I was amazed by the complexity and beauty of cells. I was intrigued to use my engineering tools to more comprehensively understand cells and control their decisions with drugs.