Clinical laboratories and point-of-care settings may have a new diagnostic test if this novel handheld device and related technology is validated by clinical trials
Efforts to develop breath analyzers that accurately identify viral infections, such as SARS-CoV-2 and Influenza, have been ongoing for years. The latest example is ViraWarn from Opteev Technologies in Baltimore, Maryland, and its success could lead to more follow-up PCR tests performed at clinical laboratories.
“Breath is one of the most appealing non-invasive sample types for diagnosis of infectious and non-infectious disease,” said Opteev in its FDA Pre-EUA application. “Exhaled breath is very easy to provide and is less prone to user errors. Breath contains a number of biomarkers associated with different ailments that include volatile organic compounds (VOCs), viruses, bacteria, antigens, and nucleic acid.”
Further clinical trials and the FDA Pre-EUA are needed before ViraWarn can be made available to consumers. In the meantime, Opteev announced that the CES (Consumer Electronic Show) had named ViraWarn as a 2023 Innovation Award Honoree in the digital health category.
“ViraWarn is designed to allow users an ultra-fast and convenient way to know if they are spreading a dangerous respiratory virus. With a continued increase in COVID-19 and a new surge in RSV and influenza cases, we’re eager to bring ViraWarn to market so consumers can easily blow into a personal device and find out if they are positive or negative,” said Conrad Bessemer (above), Opteev President and Co-Founder, in a news release.
Opteev is a subsidiary of Novatec, a supplier of machinery and sensor technology, and a sister company to Prophecy Sensorlytics, a wearable sensors company.
The ViraWarn breath analyzer uses a silk-based sensor that “traces the electric discharge of respiratory viruses coupled with an artificial intelligence (AI) processor to filter out any potential inaccuracies,” according to the news release.
Here is how the breath analyzer (mouthpiece, attached biosensor chamber, and attached printed circuit board chamber) is deployed by a user, according to the Opteev website:
The user turns on the device and an LED light indicates readiness.
The user blows twice into the mouthpiece.
A carbon filter stops bacteria and VOCs and allows virus particles to pass through.
As “charge carriers,” virus particles have a “cumulative charge.”
Electrical data are forwarded to the AI processor.
The AI processer delivers a result.
Within 60 seconds, a red signal indicates a positive presence of a virus and a green signal indicates negative one.
“The interaction of the virus with a specially designed liquid semiconductive medium, or a solid polymer semiconductor, generates changes in the conductivity of the electrical biosensor, which can then be picked up by electrodes. Such electrical data can be analyzed using algorithms and make a positive or negative call,” explains an Opteev white paper on the viral screening process.
While the ViraWarn breath analyzer can identify the presence of a virus, it cannot distinguish between specific viruses, the company noted. Therefore, a clinical laboratory PCR test is needed to confirm results.
Other Breath Tests
Opteev is not the only company developing diagnostic tests using breath samples.
For clinical laboratory managers and pathologists, Opteev’s ViraWarn is notable in breath diagnostics development because it is a personal hand-held tool. It empowers people to do self-tests and other disease screenings, all of which would need to be confirmed with medical laboratory testing in the case of positive results.
Further, it is important to understand that consumers are the primary target for this novel diagnostic device. This is consistent with investor-funding companies wanting to develop testing solutions that can be used by consumers. At the same time, a device like ViraWarn could be used by clinical laboratories in their patient service centers to provide rapid test results.
Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD (above), in the Lehigh University news release. “If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins.” This method differs greatly from traditional clinical laboratory methods for identifying disease biomarkers. (Photo copyright: Memorial Sloan-Kettering Cancer Center.)
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
Should the device prove effective, it could replace invasive point-of-care blood draws for clinical laboratory testing during patient drug therapy monitoring
What if it were possible to perform therapeutic drug monitoring (TDM) without invasive blood draws using breath alone? Patients fighting infections in hospitals certainly would benefit. Traditional TDM can be a painful process for patients, one that also brings risk of bloodline infections. Nevertheless, regular blood draws have been the only reliable method for obtaining viable samples for testing.
One area of critical TDM is in antibiotic therapy, also known as personalized antibiotherapy. However, for antibiotic therapy to be successful it typically requires close monitoring using point-of-care clinical laboratory testing.
Now, a team of engineers and biotechnologists from the University of Freiburg in Germany have developed a biosensor that can use breath samples to measure antibiotic concentrations present in blood, according to a University of Freiburg press release.
The team’s non-invasive collection method requires no needle sticks and can allow for frequent specimen collections to closely monitor the levels of an antibiotic prescribed for a patient. The biosensor also provides physicians the ability to tailor antibiotic regimens specific to individual patients, a core element of precision medicine.
“Until now researchers could only detect traces of antibiotics in the breath,” said Can Dincer, PhD (above), Junior Research Group Leader at the University of Freiburg, and one of the authors of the study, in the press release. “With our synthetic proteins on a microfluid chip, we can determine the smallest concentrations in the breath condensate and [how] they correlate with the blood values.” Should the breath biosensor prove effective in clinical settings, painful blood draws for clinical laboratory testing at the point of care could become obsolete. (Photo copyright: Conny Ehm/University of Freiburg.)
Can a Breath Biosensor Be as Accurate as Clinical Laboratory Testing?
The University of Freiburg’s biosensor is a multiplex, microfluid lab-on-a-chip based on synthetic proteins that react to antibiotics. It allows the simultaneous measurement of several breath samples and test substances to determine the levels of therapeutic antibiotics in the blood stream.
To perform their research, the University of Freiburg team tested their biosensor on blood, plasma, urine, saliva, and breath samples of pigs that had been given antibiotics. The results the researchers achieved with their device using breath samples were as accurate as standard clinical laboratory testing, according to the press release.
The microfluidic chip contains synthetic proteins affixed to a polymer film via dry film photoresist (DFR) technology. These proteins are similar to proteins used by drug-resistant bacteria to sense the presence of antibiotics in their environment. Each biosensor contains an immobilization area and an electrochemical cell which are separated by a hydrophobic stopping barrier. The antibiotic in a breath sample binds to the synthetic proteins which generates a change in an electrical current.
“You could say we are beating the bacteria at their own game,” said Wilfried Weber, PhD, Professor of Biology at the University of Freiburg and one of the authors of the research paper, in the press release.
Rapid Monitoring at Point-of-Care Using Breath Alone
The biosensor could prove to be a useful tool in keeping antibiotic levels stable in severely ill patients who are dealing with serious infections and facing the risk of sepsis, organ failure, or even death. Frequent monitoring of therapeutic antibiotics also could prevent bacteria from mutating and causing the body to become resistant to the medications.
“Rapid monitoring of antibiotic levels would be a huge advantage in hospital,” said H. Ceren Ates, PhD, scientific researcher at the University of Freiburg and one of the authors of the study in the press release. “It might be possible to fit the method into a conventional face mask.”
Along those lines, the researchers are also working on a project to create wearable paper sensors for the continuous measurement of biomarkers of diseases from exhaled breath. Although still in the development stages, this lightweight, small, inexpensive paper sensor can fit into conventional respiratory masks, according to a University of Freiburg press release.
Other Breath Analysis Devices Under Development
Devices that sample breath to detect biomarkers are not new. Dark Daily has regularly reported on similar developments worldwide.
Thus, University of Freiburg’s non-invasive lab-on-a-chip biosensor is worth watching. More research is needed to validate the effectiveness of the biosensor before it could be employed in hospital settings, however, monitoring and managing antibiotic levels in the body via breath samples could prove to be an effective, non-invasive method of providing personalized antibiotic therapy to patients.
Clinical trials on human breath samples are being planned by the University of Freiburg team. This type of precision medicine service may give medical professionals the ability to maintain proper medication levels within an optimal therapeutic window.
Federal agency hopes its open-source precisionFDA web portal will aid in development of laboratory-developed tests and inform regulatory decision-making
The FDA unveiled an open-source platform for community sharing of genetic information. It is called precisionFDA, and the FDA describes its new web platform as an “online, cloud-based portal that will allow scientist from industry, academia, government, and other partners to come together to foster innovation and develop the science” of next-generation DNA sequencing processing. (more…)
