Dr. Francis Collins
Posted on by Dr. Francis Collins
Developing faster, more convenient ways of testing for coronavirus disease 2019 (COVID-19) will be essential to our efforts to end this deadly pandemic. Despite the tremendous strides that have been made in diagnostics over the past seven months, we still need more innovation.
We need reliable, affordable tests for the presence SARS-CoV-2—the novel coronavirus that causes COVID-19—that do not take hours or days to deliver results. We need tests that are more user friendly, and that don’t rely on samples collected by swabs that have to be inserted deep into the nose by someone wearing PPE. We need tests that can be performed at the point-of-care, whether a doctor’s office, urgent care clinic, long-term care facility, or even a home. Ideally, such tests should also be able to integrate with mobile devices to convey results and transmit data seamlessly. Above all, we need tests that are accessible to everyone.
Most current diagnostic tests for SARS-CoV-2 involve detecting viral genetic material using a decades-old technology called the polymerase chain reaction (PCR). If there’s even a tiny bit of viral genetic material in a patient’s sample, PCR can amplify the material millions of times so that it can be readily detected. The problem is that this amplification process is time-consuming and requires a thermal cycling machine that’s generally operated by trained personnel in sophisticated lab settings.
To spur the creation of new approaches that can rapidly expand access to testing, NIH launched the Rapid Acceleration of Diagnostics (RADx) program in late April 2020. This fast-paced, innovative effort, conducted in partnership with the Office of the Assistant Secretary of Health, the Biomedical Advanced Research and Development Authority (BARDA), and the Department of Defense, is supported by $1.5 billion in federal stimulus funding. The goal? To expand diagnostic testing capacity for COVID-19 in the United States to about 6 million tests per day by December. That’s quite a leap forward because our nation’s current testing capacity is currently about 1 million tests per day.
Just yesterday, I joined other NIH leaders in authoring a special report in the New England Journal of Medicine that describes RADx’s main activities, and provides an update on the remarkable progress that’s been made in just three short months . In a nutshell, RADx consists of four components: RADx-tech, RADx Advanced Technology Platforms (RADx-ATP). RADx Radical (RADx-rad), and RADx Underserved Populations (RADx-UP).
Though all parts of RADx are operating on a fast-track, RADx-tech has embraced its rapid timelines in a can-do manner unlike anything that I’ve encountered in my 27 years in government. Here’s how the process, which has been likened to a scientific “shark tank,” works.
Once an applicant submits a test idea to RADx-tech, it’s reviewed within a day by a panel of 30 experts. If approved, the application moves to a highly competitive “shark-tank” in which a team of experts spend about 150 to 200 person-hours with the applicant evaluating the technical, clinical, and commercial strengths and weaknesses of the proposed test.
From there, a detailed proposal is presented to a steering committee, and then sent to NIH. If we at NIH think it’s a great idea, promising early-stage technologies enter what’s called “phase one” development, with considerable financial support and the expectation that the applicant will hit its validation milestones within a month. Technologies that succeed can then go to “phase two”, where support is provided for scale-up of tests for meeting regulatory requirements and supporting manufacture, scale-up, and distribution.
The major focus of RADx-tech is to simplify and speed diagnostic testing for COVID-19. Tests now under development include a variety of mobile devices that can be used at a doctor’s office or other point-of-care settings, and give results in less than an hour. In addition, about half of the tests now under development use saliva or another alternative to samples gathered via nasal swabs.
As Americans think about how to move back safely into schools, workspaces, and other public areas in the era of COVID-19, it is clear that we need to figure out ways to make it easier for everyone to get tested. To attain that goal, RADx has three other components that build on different aspects of this social imperative:
• RADx Advanced Technology Platforms (RADx-ATP). This program offers a rapid-response application process for firms with existing point-of-care technologies authorized by the Food and Drug Administration (FDA) for detecting SARS-CoV-2. These technologies are already advanced enough that they don’t need the shark tank. The RADx-ATP program provides support for scaling up production to between 20,000 and 100,000 tests per day by the fall. Another component of this program provides support for expanding automated “mega-labs” to increase testing capacity across the country by another 100,000 to 250,000 tests per day.
• RADx Radical (RADx-rad). The program seeks to fuel the development of truly futuristic testing technologies. For example, it supports projects that use biomarkers to detect an infection or predict the severity of disease, including the likelihood of developing COVID-related multisystem inflammatory syndrome in children (MIS-C). Other areas of interest include the use of biosensors to detect the presence of the virus in a person’s breath and the analysis of wastewater to conduct community-based surveillance.
• RADx Underserved Populations (RADx-UP). Data collected over the past several months make it clear that Blacks, Latinxs, and American Indians/Alaska Natives are hospitalized and die of COVID-19 at disproportionately higher rates than other groups. RADx-UP aims to engage underserved communities to improve access to testing. Such actions will include closely examining the factors that have led to the disproportionate burden of the pandemic on underserved populations, as well as building infrastructure that can be leveraged to provide optimal access and uptake of SARS-CoV-2 testing in such communities.
At NIH, we have great hopes for what RADx-supported research will do to help bring to an end the greatest public health crisis of our generation. Yet the benefits may not end there. The diagnostic testing technologies developed here will have many other applications moving forward. Long after the COVID-19 pandemic becomes a chapter in history books, I’m convinced the RADx model of rapid innovation will be inspiring future generations of researchers as they look for creative new ways to address other diseases and conditions.
 Rapid scaling up of COVID-19 diagnostic testing in the United States—The NIH RADx Initiative. Tromberg BJ, Schwetz TA, Perez-Stable E, Hodes RJ. Woychick RP, Bright RA, Fleurence RL, Collins FS. NEJM; 2020 July 16. [Online publication ahead of print]
Coronavirus (COVID-19) (NIH)
“NIH mobilizes national innovation initiative for COVID-19 diagnostics,” NIH news release, April 29, 2020.
Posted on by Dr. Francis Collins
Contact tracing, a term that’s been in the news lately, is a crucial tool for controlling the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19. It depends on quick, efficient identification of an infected individual, followed by identification of all who’ve recently been in close contact with that person so the contacts can self-quarantine to break the chain of transmission.
Properly carried out, contact tracing can be extremely effective. It can also be extremely challenging when battling a stealth virus like SARS-CoV-2, especially when the virus is spreading rapidly.
But there are some innovative ways to enhance contact tracing. In a new study, published in the journal Nature Medicine, researchers in Australia demonstrate one of them: assembling genomic data about the virus to assist contact tracing efforts. This so-called genomic surveillance builds on the idea that when the virus is passed from person to person over a few months, it can acquire random variations in the sequence of its genetic material. These unique variations serve as distinctive genomic “fingerprints.”
When COVID-19 starts circulating in a community, researchers can fingerprint the genomes of SARS-CoV-2 obtained from newly infected people. This timely information helps to tell whether that particular virus has been spreading locally for a while or has just arrived from another part of the world. It can also show where the viral subtype has been spreading through a community or, best of all, when it has stopped circulating.
The recent study was led by Vitali Sintchenko at the University of Sydney. His team worked in parallel with contact tracers at the Ministry of Health in New South Wales (NSW), Australia’s most populous state, to contain the initial SARS-CoV-2 outbreak from late January through March 2020.
The team performed genomic surveillance, using sequencing data obtained within about five days, to understand local transmission patterns. They also wanted to compare what they learned from genomic surveillance to predictions made by a sophisticated computer model of how the virus might spread amongst Australia’s approximately 24 million citizens.
Of the 1,617 known cases in Sydney over the three-month study period, researchers sequenced viral genomes from 209 (13 percent) of them. By comparing those sequences to others circulating overseas, they found a lot of sequence diversity, indicating that the novel coronavirus had been introduced to Sydney many times from many places all over the world.
They then used the sequencing data to better understand how the virus was spreading through the local community. Their analysis found that the 209 cases under study included 27 distinct genomic fingerprints. Based on the close similarity of their genomic fingerprints, a significant share of the COVID-19 cases appeared to have stemmed from the direct spread of the virus among people in specific places or facilities.
What was most striking was that the genomic evidence helped to provide information that contact tracers otherwise would have lacked. For instance, the genomic data allowed the researchers to identify previously unsuspected links between certain cases of COVID-19. It also helped to confirm other links that were otherwise unclear.
All told, researchers used the genomic evidence to cluster almost 40 percent of COVID-19 cases (81 of 209) for which the community-based data alone couldn’t identify a known contact source for the infection. That included 26 cases in which an individual who’d recently arrived in Australia from overseas spread the infection to others who hadn’t traveled. The genomic information also helped to identify likely sources in the community for another 15 locally acquired cases that weren’t known based on community data.
The researchers compared their genome surveillance data to SARS-CoV-2’s expected spread as modeled in a computer simulation based on travel to and from Australia over the time period in question. Because the study involved just 13 percent of all known COVID-19 cases in Sydney between late January through March, it’s not surprising that the genomic data presents an incomplete picture, detecting only a portion of the possible chains of transmission expected in the simulation model.
Nevertheless, the findings demonstrate the value of genomic data for tracking the virus and pinpointing exactly where in the community it is spreading. This can help to fill in important gaps in the community-based data that contact tracers often use. Even more exciting, by combining traditional contact tracing, genomic surveillance, and mathematical modeling with other emerging tools at our disposal, it may be possible to get a clearer picture of the movement of SARS-CoV-2 and put more targeted public health measures in place to slow and eventually stop its deadly spread.
 Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Rockett RJ, Arnott A, Lam C, et al. Nat Med. 2020 July 9. [Published online ahead of print]
Coronavirus (COVID-19) (NIH)
Vitali Sintchenko (University of Sydney, Australia)
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