Machine Learning Tool Identifies Potential Drugs to Minimize Harmful Scarring Post Heart Attack
Scientists at the University of Virginia have made a significant breakthrough in cardiac care by using machine learning to identify potential drugs that could reduce harmful scarring following a heart attack or other injuries. This cutting-edge computer model has the potential to revolutionize drug discovery for complex diseases, offering new hope for patients worldwide.
The research, led by computational biologist Dr. Anders R. Nelson and Dr. Jeffrey J. Saucerman from UVA’s Department of Biomedical Engineering, combines decades of human knowledge with a new approach called logic-based mechanistic machine learning. The interdisciplinary team focused on understanding how drugs impact fibroblasts, cells crucial for heart repair but also responsible for harmful scarring known as fibrosis.
Dr. Nelson explains, Many common diseases such as heart disease, metabolic disease, and cancer are complex and challenging to treat. Machine learning helps us reduce this complexity, identify the most important factors contributing to the disease, and better understand how drugs can modify diseased cells.
Unlike previous attempts that only focused on specific aspects of fibroblast behavior, the UVA researchers utilized their innovative machine-learning model to predict the effects of 13 promising drugs on human fibroblasts. This approach allowed them to identify a potential candidate to prevent scarring and gain insight into how it works. This dual capability is crucial in designing effective clinical trials and understanding potential side effects.
One remarkable discovery from the study is the potential of the drug pirfenidone, which is already FDA-approved for idiopathic pulmonary fibrosis. The model revealed a new explanation of how pirfenidone suppresses contractile fibers inside fibroblasts, contributing to the heart’s stiffening. Additionally, the model predicted the effects of an experimental Src inhibitor, WH4023, on another type of contractile fiber, a finding that was experimentally validated with human cardiac fibroblasts.
While further research is necessary to validate the efficacy of these drugs in animal models and human patients, the UVA team remains optimistic about the transformative potential of mechanistic machine learning. Dr. Saucerman says, We’re looking forward to testing whether pirfenidone and WH4023 also suppress fibroblast contraction in scars in preclinical animal models. We hope this provides an example of how machine learning and human learning can work together not only to discover but also to understand how new drugs work.
This groundbreaking research showcases the power of machine learning in the field of medicine, particularly in cardiac care and drug discovery for complex diseases. By harnessing the potential of this technology, scientists can gain valuable insights into the effects of drugs on specific cells and develop more targeted treatments. With further advancements and developments, machine learning has the potential to revolutionize healthcare and improve patient outcomes globally.
