NEW YORK – University of Minnesota spinout Vocxi Health is preparing to commercialize a point-of-care breath test for lung cancer that relies on highly sensitive biosensors and machine learning.
According to Vocxi CEO Ping Yeh, the company’s technology has been in the works for almost a decade, beginning with an innovation challenge at medical device manufacturer Boston Scientific. Lung cancer has proven challenging to detect because a significant portion of biopsies show nodules that aren’t cancerous, while some nodules are too small to biopsy. This sparked the question whether a home test for lung cancer could be developed to make lung cancer testing more mobile and accessible, Yeh said, adding he was approached by the technology’s creators to head the new company due to his experience creating other startups.
Working with researchers at the University of Minnesota, the Boston Scientific team aimed to determine if a new class of biosensors would be sensitive enough to pick out volatile organic compounds in the breath.
Breath-based tests have been in development for a while now, often leveraging mass spectrometry. However, the instruments are often large and unwieldy, making them impractical for home or point-of-care use, Yeh said.
In response, Yeh and his colleagues have developed nanosensors and chemistry in a “super-consistent, scalable way to selectively pick out volatile organic compound families to identify patterns” in the breath, he said.
More specifically, they have developed a wafer-based process that creates thousands of chips on a single wafer and a process to lay down a single layer of graphene on the wafers. The researchers also developed a patented process to functionalize and apply a single monolayer of receptor chemistry on top of the graphene, which Yeh said is “critical to the consistency of the sensors.”
Simultaneously, machine learning capabilities have grown in the past few years, allowing the company to take the chemical patterns from VOCs, turn them into electrical patterns, and analyze them using machine learning algorithms, Yeh said.
