Covid-19, Data and Analytics, Detection and Diagnostics

Challenges of Interpreting Test Results and Implications for COVID-19

B.Next

Diagnostic tests are a critical tool to contain epidemics, to support medical care, and for public health measures. Understanding when they are accurate and inaccurate is necessary for understanding which individuals have the virus, need isolation, and need their contacts traced.

Many diagnostic tests are reliable, though all are imperfect. And at a large scale, tiny errors in accuracy for single tests can aggregate into large errors if deployed without care. This is especially true when the rate of true infection in the tested population is expected to be low. For example, when testing for infection in a person who doesn’t have symptoms or a history of exposure, or when testing for a history of infection when the overall prevalence of disease for a given population is low.

This paper covers:

  • The accuracy and errors in diagnostic tests
  • How low disease prevalence can cause many false positives
  • Diagnostic testing in normal times: testing for influenza
  • Balancing errors with diagnostic needs
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Covid-19, Data and Analytics, Detection and Diagnostics

Leveraging Sensors to Monitor COVID-19 Symptoms Remotely

B.Next

B.Next’s experts from healthcare, government, and industry leveraged its knowledge and expansive network to create the following high-level guide that maps sensors in commercial products to key vital signs and explores ways to capitalize on the smart products that may supplement digital health efforts in response to COVID-19.

What’s Inside:

  • Connection between health sensing techniques and relevant vital signs
  • Highlight of some challenges with existing commercial solutions
  • Catalog of current product capabilities and emerging trends for future products
  • Use cases for understanding potential ways to use sensors during contingency care

This Technology Insights Guide provides an overview of the use of commercial-off-the-shelf (not medical grade) sensor technologies that might be useful in supporting the ongoing COVID-19 outbreak response. The intent of this guide is to acquaint clinicians with the fundamental concepts related to the use of wearable sensors and to provide a possible adjunct to existing healthcare related strategies for managing the potential surge of patients with COVID-19 symptoms. Emphasis on technologies that could serve as an adjunct to conventional methods of patient monitoring are provided. This effort is not intended to convey medical guidance or provide recommendations regarding the outpatient management of presumed or confirmed COVID-19 patients. It is intended as a supplement to support the ongoing efforts of the healthcare community currently managing COVID-19.

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Detection and Diagnostics

Precision Medicine 101

JJ Ben-Joseph & Dr. Dan Hanfling

This white paper covers precision medicine 101 and explores the aspirations to develop medical treatments to the individual character of each patient, applications and future directions, and precision medicine in China.

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Detection and Diagnostics

Rapid Expert Consultation on Crisis Standards of Care for the COVID-19 Pandemic

THE NATIONAL ACADEMIES OF SCIENCES, ENGINEERING, AND MEDICINE

In response to a request from the Office of Science and Technology Policy (OSTP), the National Academies of Sciences, Engineering, and Medicine convened a standing committee of experts to help inform OSTP on critical science and policy issues related to emerging infectious diseases and other public health threats. The standing committee includes members with expertise in emerging infectious diseases, public health, public health preparedness and response, biological sciences, clinical care and crisis standards of care, risk communication, and regulatory issues. This publication, co-authored by B.Next’s Dr. Dan Hanfling, articulates the guiding principles, key elements, and core messages that undergird Crisis Standards of Care decision-making at all levels.

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Data and Analytics, Detection and Diagnostics, Proof-of-Concept, Technology and Market

Poster: Secure Interrogation of Genomic Databases — ASM Biothreats 2018

B.Next, Lab41, CosmiQ Works & CyberReboot

An effective response to a disease outbreak requires the rapid identification of pathogen and source.

Exploiting genomic information has become an important component of effective biothreat agent identification, characterization, and attribution. To do so, the necessary bioinformatic analyses require known genomic data against which to compare the agent’s genomic data. However, more and more genomic data is becoming privately held. To truly understand where an agent came from or important features of the agent (e.g., virulence, alternative hosts, and environmental stability), the biodefense community will likely need to leverage the genomic data that resides in these private databases. This may be especially important when a truly novel agent is discovered and near-neighbors need to be identified. Security requirements necessary for biothreat agent information or active investigations limit the direct sharing of genomic information with outside parties. Private entities are often unable to share access to their database due to privacy and legal issues. Fortunately, technology options exist that enable secure computations to be executed that fulfill data privacy requirements.

We developed the Secure Interrogation of Genomic Databases (SIG-DB) algorithm to enable the interrogation of a privately held database with a sequence of interest to determine the presence of similar sequences, without compromising the query or database information. This method was confirmed to be functional and evaluated using wild-type and in silico mutated versions of Escherichia coli and Staphylococcus aureus genomic sequences obtained from the NCBI RefSeq database.

This is the poster that was presented at the 2018 annual biothreats meeting, hosted by the American Society for Microbiology (ASM).

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Data and Analytics, Detection and Diagnostics, Proof-of-Concept, Technology and Market

GEMstone 2.0: Secure Interrogation of Genomic Databases — Project Report

B.Next, Lab41, CosmiQ Works & CyberReboot

Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function.  Advances in synthetic biology and the low cost of sequencing are increasing the amount of privately held genomic data.  As the quantity and value of private genomic data grow, so does the incentive to acquire and protect such data, which in turn creates a need to store and process these data securely.

This project explores the limitations, opportunities, and capabilities of secure computation techniques applied to DNA sequence comparisons. Using homomorphic encryption (a software-based encryption approach) the Secure Interrogation of Genomic DataBases (SIG-DB) protocol was developed to enable searches of databases (DB) of genomic sequences with an encrypted query sequence without revealing the query sequence to the database owner or any of the database sequences to the Querier. Our results show that the SIG-DB algorithm returns an accurate assessment of the similarity of queries to databases of interest. The computational runtime and information leakage were compared between a fully homomorphic approach using the Microsoft SEAL cryptosystem and a partially homomorphic approach using the Paillier cryptosystem.  SIG-DB is the first application that we are aware of to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data.

We also explored an alternative approach that uses hardware-based secure computation, specifically Software Guard eXtension (SGX), by Intel®. We were unable to complete a prototype at this time due to the immaturity of the technology.  However, our research findings indicate that SGX has the potential to enable a cloud-based secure computation system with, theoretically, minimal information leakage and similarity scoring execution times near equivalent to plaintext comparisons.  Much research remains to be done to fully understand the operational and security limitations of the system.

We briefed government stakeholders on our prototype and findings at a recent B.Next event.  Attendees expressed strong support for continued work on homomorphic encryption for secure interrogation of genomic databases.  The participants provided valuable feedback on the tool and numerous use cases they encounter that could be transformed by this approach.  Although the algorithm was developed specifically for microbial genomics comparisons, SIG-DB could be useful for a number of applications, including healthcare, human genomics, organizational collaborations, and more.

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Detection and Diagnostics, Proof-of-Concept

GEMstone 2.0: Detecting Evidence of Genetic Engineering — Project Report

B.Next & Lab41

The desire and ability to genetically engineer organisms is becoming increasingly widespread, and the barriers to using the most sophisticated means of genome editing are falling rapidly. There is a corresponding risk that actors with malicious intent may decide to use these tools to create more dangerous strains of pathogenic organisms. The sophistication of the design tools to make such organisms currently far outstrips the capabilities of the tools with which genetic engineering can be detected quickly and accurately in an automated fashion.

In consultation with B.Next’s biodefense community partners, IQT Labs B.Next and Lab41 have explored how applying machine learning (ML) approaches to DNA sequence analysis may provide “triage” tools that enable users to quickly assess the likelihood that the genome of a suspect organism has been engineered. The Labs obtained a diverse dataset from both public (US and European) and private (a synthetic biology company) sources that was used to train, validate and test several ML models to detect the insertion of DNA from one organism or source into another. The performance of the trained models varied with the complexity of the underlying dataset, but was sufficient to illustrate the promise of ML-based approaches to rapid DNA sequence analysis for biodefense applications. Our findings also suggested immediate changes to model training that would likely improve performance.

The results of this project were recently briefed to the same members of the US biodefense community who helped frame the problem. The participants were uniformly optimistic about the potential for ML-based approaches, and recommended that future work include refinements in the data sources used and establishing confidence metrics for trained models.

Highlights:

• B.Next and colleagues in biodefense identified that the lack of analytical tools for detecting genetic engineering is a significant issue to enabling an effective response to a biothreat.

• ML has been applied to DNA analysis, but not extensively.

• The ML models developed by the IQT Labs team detect cloning boundaries – junctions between inserted DNA and the genome of the “destination” organism.

• The team created an in silico method to generate an unlimited source of synthetic cloning boundaries for use in model training and validation.

• The ML models we generated demonstrated classification accuracies between 93% and 74%, which correlated inversely with the complexity of the datasets used in training, validation and testing.

• Our work complements but does not duplicate other efforts across the USG and within IQT. B.Next-hosted discussions were the origin of IARPA’s FELIX program, which will generate larger-scale tools for detecting genetic engineering. B.Next staff were members of the source selection board for FELIX proposals.

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Detection and Diagnostics, Technology and Market

The Promise of Ubiquitous DNA Sequencing: Sequencers as Sensors

Kevin O'Connell / B.Next

Improvements in DNA sequencing technologies (which determine the order of the four constituent bases – A, G, C and T – in which the genetic code is written) have the potential to transform how we detect and respond to infectious disease.  Defense against pathogens, whether naturally occurring diseases or intentional biological attacks, depends critically on our ability to detect and identify pathogens, in order to accurately diagnose, properly triage and treat those infected, and gauge the extent and dynamics of an outbreak.  We review here the progression of DNA sequencing technologies since the 1970’s, the potential impact of sequencing on detecting and managing epidemics, and other applications that will support and expand innovations in sequencing technology.

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Detection and Diagnostics, Technology and Market

Tech Corner – A Technology Overview from IQT Portfolio Company Quanterix

In-Q-Tel, Inc. / IQT Quarterly Winter 2016

Medical laboratory analysis is moving steadily and quickly into molecular biology, in which tests can assess health based on DNA sequence, or on changes in the amount of certain proteins in blood. There are tests currently available to detect the levels of over 100 different proteins in human blood, changes in which have been correlated with changes in health or disease. Nearly all of these tests are called immunoassays because they rely on the use of immune system proteins called antibodies as the essential ingredient in the test.

Antibodies have the property of binding tightly to a specific molecular target (and only that target), giving immunoassay tests the specificity required to detect target proteins in a sea of proteins.

The most commonly used immunoassay is called ELISA (Enzyme-Linked Immuno-Sorbent Assay), which is used by medical labs and researchers to detect specific proteins of interest in liquid samples. The human genome encodes about 25,000 proteins. Of these proteins, no more than 10 percent are in sufficient concentration to be reliably measured with conventional ELISA. What clinical insights lie within the other 90 percent? Quanterix’s revolutionary Simoa technology unlocks a world of insight into disease detection, diagnosis, and patient treatment while meeting the demands of today’s laboratory.

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Detection and Diagnostics, Technology and Market

The Weakest Link in Diagnostics May Be the Sample Itself

Mark D. Lim / IQT Quarterly Winter 2016

Anyone who has had blood drawn at a doctor’s office is familiar with much of the information that results from its analysis, such as glucose, cholesterol, lipids, and cell counts. In the past two decades, two trends have given rise to a revolution in the next generation of diagnostic technologies.

First, molecular biology is increasingly finding molecules in bodily fluids (such as blood and saliva) and tissues that provide information about a patient’s health state to guide the decisions made by a physician (“biomarkers”). Second, engineering advances have enabled these tests to be performed with equipment that is smaller, less expensive, and requires less sample for analysis. These trends are creating market opportunities that, for DNA sequencing technologies alone, are predicted to exceed $20 billion with opportunities beyond niche diagnostic markets. This is encouraging more entrepreneurs to join this rapidly developing market by applying advanced analytics and novel sensors to increase the speed, precision, and accuracy for interrogating human samples, while also reducing per-analysis costs.

The same advances in diagnostic technology are finding use outside of the clinical laboratory. Less expensive, more powerful diagnostics allow public health researchers to screen larger groups of people to determine if a population is at-risk of developing or harboring disease. Such broad applications of genomic- and proteomic-based diagnostics to public health are as vulnerable as any research to flaws that prevent their reproducibility, as has been reported recently. While there are many factors that may contribute to irreproducibility in the use of diagnostic technology, there are some sources of data variability and bias that can be mitigated when engineering the work ow from human sample to molecular answers on a population scale.

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