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NCI SBIR Helps Tech Company Take Next Steps in Precision Medicine Platform via Digital Twins

A recent $2 million award to the technology firm, SimBioSys, offers a compelling example of how NCI’s Small Business Innovation Research (SBIR) program is working to advance cancer research.

Voted one of 2022’s best tech startups in Champaign, IL, the heart of the Illinois corn belt, SimBioSys has been steadily making inroads in the use of precision medicine for predicting, planning, and treating cancer.

Specifically, SimBioSys researchers have been working on a platform using computational models to create a “virtual tumor” (and surrounding tissues). That platform allows researchers to predict how cancer will progress and to plan the best treatment based on the patient’s own unique biology.

In 2021, NCI SBIR enabled SimBioSys developers to launch PhenoScope, a prototype cloud-based application for managing and integrating a wide range of data—genetic, cellular/system mechanisms, clinical, outcome, etc.—to identify biomarkers of disease.

Now, with additional SBIR funding, SimBioSys will use this Phase II contract to take the next steps in developing their biophysical simulation technology for personalized cancer care, especially in the area of time-and-space algorithms. With these innovations, the developers hope to be able to predict how a tumor will respond 2, 4, and even 12 months in the future. That information will be vital for future drug development efforts.

“An explosion in cancer data paired with ever-growing computational complexity in analyzing such data can hinder insight into our understanding of cancer development,” said Dr. Joseph Peterson, Chief Technology Officer of SimBioSys. “By creating virtual digital twins that span biological scales, PhenoScope will allow researchers to more simply test complex hypotheses.”

According to the developers, their ultimate goal is to link the application to patients’ electronic health records. Oncologists then would be able to seamlessly navigate to the cloud-based platform and automatically receive feedback for planning patient care. This would enable clinicians to identify how a patient will respond to a particular drug, at a certain dose, and over time.

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