SBIR Phase I: A Blood Test to Detect Cerebral Aneurysms

This Small Business Innovation Research (SBIR) Phase I project is expected to advance the diagnosis and treatment of cerebral aneurysm (CA). CAs affect 2-5% of the population. Nearly 30,000 Americans each year suffer CA rupture without warning, resulting in approximately 50% mortality. CAs are largely asymptomatic, and therefore usually undetected until ruptured. By providing the first blood test able to detect and evaluate CAs, this project will enable monitoring at frequencies not possible today. The technology will also offer a dynamic rupture risk score that can be integrated into the patient care routine to better guide preoperative, invasive diagnosis and surgical interventions. Decreased testing costs enabled by this technology will promote more regular monitoring and early action, benefiting minorities and other groups with lower socioeconomic status who struggle to access preventative healthcare. Ultimately, this project has the potential to lead to improved patient outcomes and better quality of life for patients living with unruptured CAs and reductions in healthcare costs, as well as new insights into CA pathogenesis. The technology will bring peace of mind to those in high-risk groups and their families. This Small Business Innovation Research (SBIR) Phase I project seeks to advance the first simple, whole blood-based diagnostic test to detect the presence and monitor the progression of a cerebral aneurysm (CA). The project will develop a dynamic rupture risk score as well as novel aneurysm subgroupings. Currently, CAs can only be diagnosed with cerebral imaging such as magnetic resonance imaging or computed tomography angiography. These approaches are not suited for regular screening due to prohibitively high costs and potential risks. This project will exploit the fact that aneurysms are dynamic and exhibit different cytokine signatures over time. With a carefully selected panel of cytokines and a proprietary model, these inflammatory signatures can be reliably differentiated in CA patients with unruptured and ruptured aneurysms. This project will generate a robust dataset of CA patient blood samples, with a focus on increasing sample representation from underserved populations. The dataset will be used to train a proprietary probabilistic equation to develop a risk of rupture metric. Data will be stratified using machine learning-based principal component analysis to create distinct aneurysm subgroups with key cytokines of interest. This analysis will open the door for precision-medicine molecular therapy against specific drivers of inflammation in those patients. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.