Rapid, low cost, smart phone enabled pandemic testing in real time

A portable, household biosensor for rapid virus detection, enabling mass population testing and pandemic tracking in real time.

The solution is developed by an interdisciplinary team with expertise in two complementary science areas of quantum technologies and bio-diagnostics, led by Dr. Olga Kazakova and Dr. Max Ryadnov respectively.

The solution operates at the interface between the challenge areas of “identify” and “respond”. The current COVID-19 pandemic has shown how quickly the rapid rise in demand for testing overwhelmed existing infrastructure and capabilities, whose performance was limited by the need for specialised instrumentation, trained personnel, and established supply chains. In times of peak demand this dependence becomes a bottleneck for progress, but also stimulates the development of alternative solutions, which are deskilled and can be re-purposed and re-deployed to respond to epidemics faster and at a lower cost.

This solution offers exactly that and brings the detection of the virus and its transmission to the level of mass testing in real time. The solution is a portable, personal sensor inserted into a smartphone for use in households and points of entry. The sensor is designed to detect the virus within minutes and registers obtained data in an app, e.g. a symptom tracker. The impact of this solution is to (1) provide a simple, low cost, rapid, smartphone-enabled personal sensor, which (2) allows public health organisations to access mass scale geo-data obtained in real time to track, measure and control disease transmission.

The solution serves the need for mass population testing, including the rapid turnaround of data collection, connecting households, testing centres and points of entry with public health organisations, who would be able to locate disease outbreaks and monitor disease transmission. The solution provides benefits to the end user and early adopters of the underpinning technology. It enables individuals to monitor their health and risk of spreading infection, helps health organisations to track the disease and improve their response to other challenges requiring timely testing and tracking. As a UK based organisation we are working with Public Health England to understand their current and future diagnostic needs. The solution also provides a quality validation platform for the developers of commercial tests, supported by the infrastructure of the national standards institute, and can be adapted to replicate in settings other than public health, e.g. for defence personnel. The solution will integrate data capabilities contributing to digital health at two levels: the sensor itself, e.g. data processing upon acquisition to filter noise signals, and a digital data infrastructure, e.g. anonymised geo-data, collected and connected by tracker apps. Microsoft have offered in-kind support and advice to develop such capabilities for the solution.

The solution provides public good in several key ways, firstly: knowledge – publications, white papers, best practice guides and an ISO technical standard or report; data – open-source reference datasets for test developers under FAIR principles, connected health data and diseases transmission data stored and linked to NHS or other healthcare organisations; services – fee-based consultancy and measurement services, subcontract partnerships to validate other diagnostic and data management solutions. The first public goods in the area build upon the NPL’s mission to provide measurement standards for industry and safeguard the reproducibility and traceability of data. With new regulations coming into force, which require industry to demonstrate traceability of their products and technologies, the solution would pre-empt (post-project) the demonstration of traceability by offering SI-traceable measurements and materials to the end user who develop diagnostic solutions. The solution will also serve as an exemplar demonstrator for the application of second-generation quantum and artificial intelligence technologies in the life sciences and healthcare sector, thus helping to accelerate the contribution of such technologies to public service sectors.

The solution does not discriminate between populations or subpopulations contributing to the well-being of all. This project will bring the solution to the level of a clinically validated PoC device to allow a tangible impact of population testing, which will be two-fold. Firstly, the device will be scaled up in partnership with industry (manufacturing). This will rely on those outputs of the project, which demonstrate the reliability of data for clinical samples using the experimental format of the device. Optimisation studies will inform the final, kit-like, production unit, which will also be subjected to pilot evaluations by industry partner(s). Secondly, the solution will be rolled out to enable population testing and disease transmission monitoring. This will be subjected to impact evaluations by relevant organisations, with whom relationships have been established, e.g. PHE, DSTL and Ploughshare Innovations, who will assess the scale of testing using the device. This is also envisaged to expand the first cohort of trials in clinic (within this project) to a larger cohort to pre-modulate the impact of mass population testing. An additional impact will be assessed for the device as a validation platform to benchmark the development of other commercial tests at different TRLs.

The solution will start demonstrating a transformational impact from the first year following the project completion. The project results will be used to scale impact, which will be evaluated with follow-up partners (manufacturing, digital health) and advisors (PHE, DSTL, Microsoft) during the project. Scaling up is in the core of the solution after the project completion (years 1-2). Preliminary discussions were held with relevant organisations to increase output, including large cohort testing (PHE, DSTL) and safe data handling and processing (Microsoft). Scaling out is factored in the development of the solution to replicate it in other settings which require mass testing for complementary purposes (e.g. defence) (year 2). Scaling deep concerns the solution as a quality validation platform to benchmark commercial tests that use same underpinning technologies (graphene-based conductivity, antibody-antigen detection) (year 3). Scaling and trade-offs will be defined during the project, which will give a portable product design and the first production unit, with sensitivity and specificity evaluated and documented in clinic. The unit is ready for deployment to relevant organisations (PHE, DSTL) for large cohort testing (first) following by the first, real-life testing rounds, before launching a household kit (year 2-3).

The first measure of success for the solution, achieved in the project, is to reach the appropriate accuracy and reproducibility of virus detection in patient samples. This will be judged by the common performance metrics (sensitivity and specificity, confidence levels, false negatives, and positives). Post-project, success indicators against impact goals include increased outputs of the solution measured by the generation of statistically more significant data obtained in trials for larger cohorts of patient samples and by the demonstration of safe data handling and processing in data transfer pilot studies. Similar metrics will be used to monitor the impact in complementary, non-public health settings, which require the same levels of mass testing. The success in using the solution as a quality validation platform is evaluated by the number of commercial tests helped. The success of a portable production unit is measured by the number of distributed units for the first, real-life testing rounds, and followed up by the number of sold sensors at the set low cost. An ultimate indicator of the first demonstrable impact is the adoption of the product with the first 1 million individual datasets connected via a track app. 

The research will optimise the solution against technical parameters necessary for a portable and reproducible PoC device. All technical optimisations will be completed by the end of the project (year 1), including (i) clinically relevant data on LoD, sensitivity and specificity with false negatives and positives, (ii) data processing algorithms and fidelity, (iii) final detection format, sensitivity of detection with signal amplification, (iv) the production unit design. The first production unit will require financing for scaling, which will be pursuit with established advisors via Ploughshare Innovations as the project progresses. Manufacturing for scale up and out, following pilot impact evaluations during the project, will be also pursued with industry partners, to overcome the barrier of cost reduction. Materials cost per unit (graphene, antibodies) is the most significant barrier for this and any other analogues solutions. Scaling will address cost optimisations (input versus output), but mass testing will depend on establishing a manufacturing chain, which is not time, but capacity dependent. Other barriers, cultural and policy, are not envisaged to limit the impact. Legal and market barriers are not unique for this solution, while there is no clear leader in portable devices and the demand for such solutions exceeds the offer.

There are multiple reasons for NPL to apply to the Trinity challenge. The initial benefit of the challenge was the strength of the foundation members and the expertise in digital health and global data solutions. The project that we have outlined in this proposal will require significant partnership to be widely adopted. NPL as the UK’s national measurement laboratory has extensive experience around understanding and characterising sensor performance, but has less experience in digital health solutions.

NPL has already taken advantage of discussing the project with challenge members and have received offers of in-kind support around data analysis and management.
As a government owned laboratory our funding is controlled, planned and agreed well in advance. For immediate challenges and urgent or unexpected market needs we rely on grant applications and challenges like the Trinity.

We see this challenge as a way of fast tracking the development, developing partner relations, and finding ways of getting the technology to point of success or failure as fast as possible.

As a research laboratory, NPL has experience in partnership with a wide range of organisations from both public sector and industry. In particular from the list of Trinity partners (but not limited to): 

Microsoft: As discussed in previous sections, experience in health based datasets and secure handling of confidential information.

Becton Dickinson: As a medical device manufacturer, BD’s experience in medical device, regulations, manufacturing and supply chain, would be invaluable to this project