The National Institutes of Health is fast-tracking funding for development of novel, non-traditional approaches to identify the current SARS-CoV-2 virus or other biomarkers of unknown future pandemic pathogens.
Funding is available for innovative portable device development to enable reliable associations between biomarkers emanating from skin and the oral cavity to patients with symptomatic and asymptomatic COVID-19.
Specifically, biosensing devices are expected to target skin or the oral cavity as sampling sites. Skin biosensing must detect volatile organic compounds (VOCs, i.e. scents or odors) emanating from skin in passive and noninvasive manner for use at the point of care. Oral biosensing technologies may target a wealth of biological, chemical (e.g., VOCs) and physical biosignatures representative of COVID-19 that can be sampled from exhaled breath/droplets, saliva, and tissues in the oral cavity.
For VOC monitoring, sensing devices can be Electronic-nose (E-nose) technology or Gas Chromatography (GC).
The effort is part of an expedited funding initiative called Rapid Acceleration of Diagnostics-Radical (RADx-rad). The RADx initiative is a national call for scientists and organizations to bring forward their innovative ideas for new COVID-19 testing approaches and strategies.
SCENT and Oral Cavity Biosensing Research Objectives
With the tentative opening of many States came an increase in COVID-19 cases, there is a critical need for non-traditional testing technologies that are non-invasive, not reagent intensive, and that do not take a long time to gather results.
Current testing technologies are not practical for field use, requiring expensive reagents and enzymes and laboratories certified for potentially virulent samples. These tests are cumbersome to perform because they use aqueous solutions, require multiple steps and hours, if not days, to get results.
The SCENT device is envisioned to be used in a hospital, clinic setting, community or even home and workplace. For example, instead of taking temperatures at entrances to establishments, SCENT can be used for more informative and accurate data. The danger of contamination is minimal as SCENT will probe the skin with minimal to no potential exposure to the virus. In addition, the key substrate for SCENT will be VOCs, i.e., scents or odors emanating through skin, which are easier to standardize with, at least, two ways to account for person-to-person differences in skin permeability, namely Total Evaporative Water Loss (TEWL) or skin impedance.
The oral cavity provides another alternative for SCENT VOC detection because it is readily accessible. For example, exhaled breath could be captured and analyzed for direct detection of the respiratory tract infection from unique volatile organic metabolite byproducts of SARS-CoV2 infection.
VOCs from skin and oral cavity offer opportunities for continuous (i.e., wearable) or periodic monitoring of viral infection and disease presentation. The recent advances in biosensing, micro-electromechanical systems (MEMS) and nanotechnology combined with artificial neural networks, artificial intelligence (AI)/machine learning and smart phone technologies could make such a portable, multifunction device a reality. The innovation/challenge is to combine these technologies into devices that measure VOCs on skin and/or oral cavity, subsequently correlating those VOC patterns with COVID-19 signatures through AI/machine learning.
In the current COVID-19 epidemic, this quick screening device would enable doctors to detect and diagnose COVID-19 symptomatic and asymptomatic individuals leading to appropriate treatment and/or quarantine procedures. Additionally, a SCENT platform may be able to differentiate between COVID-negative and COVID-positive-asymptomatic subjects.
At the London School of Hygiene and Tropical Diseases, dogs are being trained to detect the scent of potential COVID-19 patients. This is possible because the dog’s olfactory system contains 300 million receptors whereas the human nose has only 5 million receptors. The central premise of SCENT-based devices is their ability to mimic the biological sense of smell with a more robust, standardized and mechanized electronic nose. For example, unique VOC skin signatures are already identified in symptomatic and asymptomatic malarial infections vs. uninfected cases. In the long run, the SCENT platforms are not limited to COVID-19 diagnosis, and can be readily adapted to other pandemics, as well as for the detection of other diseases and conditions. The potential is limited only by the development and availability of the training and validation data sets for VOC signatures that will be used for the machine learning competency.
In addition to VOC detection, the oral cavity provides unique opportunities to enhance virus testing capacity by developing new ways to collect and measure samples for rapid and accurate detection of a wide range of host-specific biomarkers that characterize manifestations of COVID-19 according to reliable biologic, physical and chemical responses. Such biomarkers may be predictive of the severity of the disease, its co-morbidities and its progression and outcome.
A variety of existing and emerging biosensing technologies can leverage available analytical methods to develop new diagnostic strategies by restructuring their sensing module for the detection of biomolecules, especially nano-sized objects such as protein biomarkers and viruses. Current sensing platforms for SARS-CoV-2 may require continuous updates to address growing challenges in the diagnosis of COVID-19 as the virus could change and spread largely from person-to-person, indicating the urgency of early diagnosis. Oral biosensing technologies may include several major functional modules optimized for COVID-19 detection, such as:
- sensing bioreceptor
- detector with readout for visual display
- secure integration of interoperable features with accessory clinical internet-of-things (IoT) systems and digital platforms
It is highly desirable that Quality by Design (QbD) principles, and available crowdsourced data on the SARS-CoV-2 virus and COVID-19 disease, are leveraged in early research and development of oral biosensing technologies to employ a holistic strategy that accounts for possible end-state manufacturing, production, and usability milestones.
Rather than relying on finished product testing alone, leveraging early identification of critical product attributes and process parameters to drive preclinical development will increase the likelihood of success in meeting clinical performance requirements with cost-effective scalability and deployability.
Specific approaches of interest for oral biosensing may include, but are not limited to:
- Develop and optimize novel oral biosensors using various methods of detection mechanisms, such as: optical, electrochemical, piezoelectric, magnetic, micromechanical, thermal, acoustic, and others. For example, imaging approaches for label-free, or smart-targeted exogenous, contrast for localized detection of COVID-19 specific biomarkers in the oral cavity, or oral intradermal mucosal patches to detect local and systemic infection levels to facilitate identification of new early stage diagnostic sentinels. Changes in the lips and oral cavity environment, such as blood supply, oxygenation, inflammatory and immune response, sense of taste, volatile metabolites and other presentations could be monitored.
- Integrate oral biosensors into a variety of form factor designs and configurations (disposable vs multi-use) suited for health/dental care environments and at home-use, including toothbrushes, dental instruments, and appliances (e.g. orthodontic brackets, crowns, dental implants, mouthguards) for detection, monitoring, and diagnosis of COVID-19. These integrated oral biosensors could be optimized not only to detect traditional biomarkers (virus and antibody), but also for additional biomolecular signatures predictive of COVID-19 disease onset, progression and resolution.
- Integrate and ensure interoperability with RFID enabled technologies, blockchain, artificial intelligence, and machine learning systems for automatic detection, secured data exchange, and provenance and traceability controls for disease tracking across multiple data streams. Ensure compatibility of test readouts with crowdsourcing Apps to add power to worldwide data pool to more effectively track the infection and symptoms and accelerate translational research