Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease.
Publication Year:
2021
PubMed ID:
33303684
Funding Grants:
Public Summary:
Understanding how genes interact with each other in human diseases can help us develop treatments that target the root causes of these diseases. However, when testing potential drugs, researchers typically focus on only a few specific effects, which can limit the chances of finding drugs that truly modify the disease. In this study, we used a computer-based approach called machine learning to identify small molecules that can correct the gene networks that go awry in aortic valve disease. We used a disease model created from human cells that have been reprogrammed into a versatile state called induced pluripotent stem cells. We found a promising candidate called XCT790, which not only corrected the gene networks in the heart disease model but also worked in cells directly taken from patients and in a mouse model of the disease. This approach, made possible by advanced technology and analysis methods, could be an effective way to discover new drugs for various diseases.
Scientific Abstract:
Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.
