The lab of Penn’s César de la Fuente sits at the interface of machines and biology, with much of its work focused on innovative treatments for infectious disease. When COVID-19 appeared, de la Fuente and his colleagues turned their attention to building a paper-based biosensor that could quickly determine the presence of SARS-CoV-2 particles from saliva and from samples from the nose and back of the throat. The initial iteration, called DETECT 1.0, provides results in four minutes with nearly 100% accuracy.
Clinical trials for the diagnostic began Jan. 5, with the goal of collecting 400 samples—200 positive for COVID-19, 200 negative—from volunteers who also receive a RT-PCR or “reverse transcription polymerase chain reaction” test. This will provide a comparison set against which to measure the biosensor to determine whether the results the researchers secured at the bench hold true for samples tested in real time. De la Fuente expects the trial will take about a month.
If all goes accordingly, he hopes these portable rapid breath tests could play a part in monitoring the COVID status of faculty, students, and staff around Penn.
Taking on COVID-19 research in this fashion made sense for this lab. “We’re the Machine Biology Group, and we’re interested in existing and emerging pathogens,” says de la Fuente, who has appointments in the Perelman School of Medicine and School of Engineering and Applied Science. “In this case, we’re using a machine to rapidly detect SARS-CoV-2.”
To this point in the pandemic, most SARS-CoV-2 diagnostics have used RT-PCR. Though effective, the technique requires significant space and trained workers to employ, and it is costly and takes hours or days to provide results. De la Fuente felt there was potential to create something inexpensive, quicker, and, perhaps most importantly, scalable.
With support from the inaugural Nemirovsky Prize given by Penn Health-Tech and funds from the Perelman School of Medicine Dean’s Innovation Fund, the research team repurposed a technology the lab frequently uses, this time creating chips that capture the chemical information generated when the SARS-CoV-2 spike protein binds to its natural receptors. “Our technology transforms that chemical information into an electrical signal that we can detect very easily,” he says.
DETECT is made of cardboard, which can be recycled, though the lab has also built chips out of paper and polymers. “Playing around with different materials allows us to select those that are the cheapest but still work well,” de la Fuente says. Once coated with saliva or material from the nose and back of the throat, the chip goes into a device that’s plugged into a phone. An app then reads the sample and provides results.
“This technology is very sensitive,” he says. Specifically, the research team analyzed its accuracy, sensitivity, and specificity. The latter two are statistical measures that go hand in hand. Sensitivity looks at the number of “true positives,” in this case how often a sample that actually does contain COVID-19 is detected as COVID-positive. Specificity focuses on “true negatives,” which, for COVID-19, means how often a sample that does not contain virus is registered as virus-free. DETECT performed well in all three measures.
“The other interesting thing about the technology is that it’s highly scalable,” de la Fuente says. “With a screen printer, you can print about 50,000 electrodes per day.” Each electrode equates to one test, so with 10,000 such printers, this could scale up to 15 billion electrodes daily or 15 billion tests.
Beyond that, each test costs less than $5 and produces results within four minutes, factors that give de la Fuente hope that, down the line, anyone might be able to use this rapid COVID test at home. “Having low-cost tests expands the reach of any diagnostic not only to people who can afford it but to remote areas and to more disadvantaged communities,” he says. “The speed of detection allows for high-frequency testing, which is a current gap, particularly in preventing asymptomatic spread.”
This type of diagnostic tool can theoretically work for many different kinds of pathogens, not just SARS-CoV-2. “The innovation goes beyond COVID-19,” says de la Fuente. “Maybe it could apply to flu and drug-resistant bacterial pathogens. Really you could find any binder to any infectious agent and incorporate it onto the chip. Any event where you have a binding process or mechanism, we can capture that, at least conceptually. We’ve done a lot of work on this already and we’re doing more. For the next pandemic, we’ll be ready.”
César de la Fuente is a presidential assistant professor and leader of the Machine Biology Group. He has appointments in the Perelman School of Medicine and School of Engineering and Applied Science at the University of Pennsylvania.
