NIH Institutes and Centers likely to participate include some or all of the following:
Office of the Director (OD)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
National Cancer Institute (NCI)
National Center for Advancing Translational Sciences (NCATS)
National Center for Complementary and Integrative Health (NCCIH)
National Heart, Lung, and Blood Institute (NHLBI)
National Institute of Allergy and Infectious Diseases (NIAID)
National Institute of Dental and Craniofacial Research (NIDCR)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
National Institute of Environmental Health Sciences (NIEHS)
National Institute of General Medical Sciences (NIGMS)
National Institute of Mental Health (NIMH)
National Institute of Nursing Research (NINR)
National Institute on Aging (NIA)
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
National Institute on Deafness and Other Communication Disorders (NIDCD)
National Institute on Drug Abuse (NIDA)
National Institute on Minority Health and Health Disparities (NIMHD)
National Library of Medicine (NLM)
Office of Research on Women's Health (ORWH)
Office of Strategic Coordination (Common Fund)
The purpose of this Notice is to alert the community that NIH plans to publish multiple Funding Opportunity Announcements (FOAs) as part of the Rapid Acceleration of Diagnostics - Radical (RADx-rad) initiative to support new, non-traditional approaches and new or non-traditional applications of existing approaches addressing current gaps in COVID-19 testing. The FOAs are expected to be published throughout July, and we will accept applications September 2020 for FY21 funding. The FOAs will be Requests for Applications (RFAs) for various grant mechanisms and Notices of Special Interest (NOSIs) for competitive revisions. The goal is to make awards by December 2020.
This Notice is being provided to allow potential applicants additional time to develop responsive applications.
Research Initiative Details
The goal of RADx-rad is to support new, non-traditional approaches, including rapid detection devices and home-based testing technologies, that address current gaps in COVID-19 testing. The program will also support new or non-traditional applications of existing approaches to make them more usable, accessible, or accurate. This may include unconventional screening, biological or physiological markers, new platforms, and point-of-care devices, and data science and artificial intelligence technologies that address current gaps in COVID-19 testing. Despite the variety of activities included, the overall RADx-rad effort will be centrally aligned and coordinated to harmonize the data collection, storage, and management, providing an opportunity to further explore and identify additional approaches to understand this novel virus. Advances through RADx-rad may also be foundational to informing a more expedient research response to future emerging pathogens.
NIH plans to publish RFAs and NOSIs related to the following RADx-rad topics:
- Wastewater detection of SARS-COV-2 (COVID-19) Wastewater-based testing (WBT) surveillance can provide detailed mapping of the extent and spread of COVID-19 and has been shown to be orders of magnitude cheaper and faster than clinical screening, albeit serving as a complementary approach rather than substituting individual-level testing and screening. The NIH may solicit both cooperative agreements for field studies and small business research and development projects. Applications should address topics such as:
- Investigation and demonstration of specific approaches aiming to inform and optimize sample collection.
- Demonstration of effective and optimized sample analysis approaches
- Implementation and development of optimized approaches to extrapolate estimation of population-level data within the community.
- Development of optimized intervention strategies.
- Incorporation of computational, statistical, and mathematical models.
- Exosome-based Non-traditional Technologies Towards Multi-Parametric and Integrated Approaches for SARS-CoV-2 -- Newly developed technologies for single vesicle, exosome, and exRNA isolation and analyses will be repositioned for the detection of SARS-CoV-2, a coronavirus that has similar physical and chemical properties as exosomes, and will utilize non-invasive, Point-of-Care sample collection from body fluids, such as saliva or urine to develop noninvasive, reliable, and reproducible covid-19 tests. Awardees will be focused on proof of concept and validation of this approach. In particular, newly developed technologies and approaches for single exosome and exRNA isolation and analyses will be deployed for detection of SARS-CoV-2 virus RNA and/or protein, and detection of IgA, IgG, and IgM antibodies against the virus. Also, technologies and analytical assay approaches will be validated against a cohort of COVID-19 patients and unaffected individuals
- Chemosensory Testing as a COVID-19 Screening Tool -- This project will support chemosensory testing as a COVID-19 screening tool by using objective tests to encourage the development and/or deployment of home-based and on-site chemosensory tests. Areas of interest include:
- Development and deployment of standardized and validated over the counter testing kits and tests that utilize common household items for remote, home-based screening through telemedicine for mild to moderately affected individuals.
- Modification of existing test platforms (e.g. NIH Toolbox odor identification test, taste strips test, etc.) to improve efficiency of administration, data collection and evaluation using mobile phone apps and telemedicine.
- Development of innovative chemosensory platforms that can be implemented for testing of older adults and caretakers residing/working at nursing homes and long-term care facilities.
- Establishment of appropriate odorants, optimal odorant concentrations, and standardized delivery systems and protocols for the development of onsite, group testing stations for those working or living in high-density, high risks environments
- Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence (PreVAIL kIds) This project will address a critical knowledge gap about the contributing factors associated with increased susceptibility to MIS-C and how these data can be used in real-time to implement risk stratified management strategies. The NIH may solicit R61/R33 applicants. Applicants will work to develop a multi-faceted and non-traditional approach, including novel testing strategies and technologies (e.g. diagnostic and prognostic biomarkers and/or biosignatures), used alone or in combination (e.g. artificial intelligence [AI]-based algorithms) to rapidly diagnose and characterize MIS-C associated with SARS-CoV-2 and to accurately risk-stratify and predict disease severity throughout exposure and/or infection and throughout the course of illness in infants, children and adolescents (<21 years). These approaches, strategies and technologies that may lead to an accurate and reliable MIS-C prognostic algorithm to enhance the treatment of children must ideally include all or as many of the following areas, as possible:
- Genetics, Omics and Other Biomarkers
- Viral Dynamics and Immune Profiling Studies
- Digital Health Platforms Leveraged for Children
- Artificial Intelligence Platforms
- Multimodal COVID-19 surveillance methods for High Risk Populations in densely populated facilities This Funding Opportunity Announcement (FOA) will focus on developing a platform that brings together data from existing surveillance technologies, not based or focused on direct viral testing, to facilitate early detection of COVID-19 in facilities at high risk for infections due to the high density of individuals who are together for prolonged periods of time (e.g., receiving treatment such as at dialysis centers) or those living together, such as in senior living systems, jails and prisons, residential treatment facilities, halfway houses, Group Homes for Persons with Intellectual Disabilities (GHPID), shelters or similar. There are numerous technologies that have already been developed which allow for some sophisticated surveillance inputs. However, these technologies are often not interoperable, not researched to optimize the best possible combinations and not tested for general applicability to public health, or for the specific need of high-risk population surveillance. The first stage of this effort will identify existing surveillance technologies that can be rapidly deployed in communal living contexts and secure participation of community and industry partners. Next, researchers will be expected to propose robust, local, real-time, accurate and cost-effective surveillance approaches that demonstrate innovative integration of the identified toolkit of technologies. In addition to functional surveillance, these projects may also explore novel methods of data collection and interpretation and use of machine intelligence to facilitate broad-based, real-time assessment, along with data modeling, prediction and visualization (which would require the use of standard vocabulary and common data elements (CDE), all in the context of addressing the needs of high risk, vulnerable populations. Potential research questions to be addressed include:
- What are the possible health targets amendable to the multimodal surveillance in densely populated facilities?
- What combination of surveillance modalities allows for the earliest identification of possible cases in pre-symptomatic and asymptomatic cases?
- How to merge, validate and analyze the data in order to have a detailed view on the evolution of COVID-19 within the residential institutions?
- Novel Biosensing for Screening, Diagnosis and Monitoring of COVID-19 From Skin and The Oral Cavity - This initiative seeks to advance the development of novel biosensing detection technologies and their integration with dedicated engineering and artificial intelligence systems that enable clinical translation of safe and effective capabilities for biosensing that leverages the accessibility of human skin and the oral cavity to detect biological, chemical and other biometric signatures of COVID-19. This initiative encourages the implementation of such technologies by integrating them into everyday settings and routines for detection, diagnosis, prediction, prognosis and monitoring in clinical, community or applied settings. Volatile organic compounds (VOCs) are released through the skin and oral cavity and can represent signatures of health and disease. The accessibility of the skin and oral cavity provides strategic advantages for designing non-invasive technologies and approaches for real-time, continuous or periodic, measurements to aid the identification of immune pathways that might be dysregulated in COVID-19, thereby predicting the level of severity during the course of the disease, and build rationale for specific treatment strategies. It is envisioned that these technologies will enable not only traditional rapid virus and antibodies detection, but also for highly sensitive olfactory biosensors for the detection of VOCs into integrated Scanning Covid-19 with Electronic Nose Technology (SCENT) platforms, and novel oral biomolecular signatures of onset, progression, and resolution of COVID-19. Biosensing technologies can include several major functional modules optimized for COVID-19 detection, such as: 1) sensing bioreceptor; 2) transducer; 3) detector with readout for visual display; and 4) 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 data on the SARS-CoV-2 virus and COVID-19 disease, are leveraged in early research and development of SCENT and 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
- Automatic detection and tracing of SARS-COV-2 - This project will support the early stage development of an innovative platform that integrates aptamer biosensing with touchscreen or other digital devices to achieve real-time detection and tracing of SARS-CoV-2. The project needs to demonstrate proof-of-concept of SARS-CoV-2 detection with high sensitivity and specificity, immobilized sensor functionality, automatic signal transduction and detection by digital devices. The NIH may support proposals using the U01 mechanism that aim to establish the proof-of-concept by:
- Identifying and characterizing aptamers with high affinity and specificity to SARS-Cov-2;
- Validating the functionality of the immobilized sensor; and
- Demonstrating effective signal transduction that can be captured by the digital device
RADx-rad will also have a Data Coordination Center (DCC) which will provide management, direction, and overall coordination across RADx-rad awardees in areas such as data sharing, data management standards, terminologies, and common data elements. The NIH expects awardees of the various RADx-rad funding opportunities to work closely with the DCC to submit data. Awardees should also use guidance provided by the DCC for data acquisition, collection, and curation, including appropriate consent for data sharing, use of Common Data Elements https://cde.nlm.nih.gov/, and appropriate metadata standards.
Funding Information
Estimated Total Funding: TBD
Expected Number of Awards: TBD
Estimated Award Ceiling: TBD
Primary CFDA Numbers: 93.310
Anticipated Eligible Organizations
Public/State Controlled Institution of Higher Education
Private Institution of Higher Education
Nonprofit with 501(c)(3) IRS Status (Other than Institution of Higher Education)
Nonprofit without 501(c)(3) IRS Status (Other than Institution of Higher Education)
Small Business
For-Profit Organization (Other than Small Business)
State Government
County governments
City or township governments
Special district governments
Indian/Native American Tribal Government (Federally Recognized)
Indian/Native American Tribal Government (Other than Federally Recognized)
U.S. Territory or Possession
Independent school districts
Public housing authorities/Indian housing authorities
Native American tribal organizations (other than Federally recognized tribal governments)
Regional Organization
Non-domestic (non-U.S.) Entities (Foreign Institutions) are eligible to apply.
Non-domestic (non-U.S.) components of U.S. Organizations are eligible to apply.
Foreign components, as defined in the NIH Grants Policy Statement, are allowed.
Applications are not being solicited at this time.
Please direct all inquiries for the relevant project to the respective contact below:
Wastewater detection of SARS-COV-2 (COVID-19)
National Institute on Drug Abuse (NIDA):
Leonardo Angelone, Ph.D.
301-827-5946
leonardo.angelone@nih.gov
National Library of Medicine (NLM):
Valerie Florance, Ph.D.
301-496-4621
florancev@mail.nlm.nih.gov
Exosome-based Non-traditional Technologies Towards Multi-Parametric and Integrated Approaches for SARS-CoV-2
National Center for Advancing Translational Sciences (NCATS):
Danilo Tagle, Ph.D.
301-594-8064
danilo.tagle@nih.gov
Chemosensory Testing as a COVID-19 Screening Tool
National Institute on Deafness and Other Communication Disorders (NIDCD):
Susan Sullivan, Ph.D.
301-451-3841
sullivas@nidcd.nih.go
National Institute of Dental and Craniofacial Research (NIDCR):
Amanda Melillo, Ph.D.
301-312-9037
amanda.melillo@nih.gov
Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence (PreVAIL kIds)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD):
Bill Kapogiannis, M.D.
301-402-0698
kapogiannisb@mail.nih.gov
Multimodal COVID-19 surveillance methods for High Risk Populations
National Institute on Drug Abuse (NIDA):
Elena Koustova, Ph.D., MBA
301-496-8768
koustovae@nida.nih.gov
Novel Biosensing for Screening, Diagnosis and Monitoring of COVID-19 From Skin and The Oral Cavity
National Center for Advancing Translational Sciences (NCATS):
Danilo Tagle, Ph.D.
301-594-8064
danilo.tagle@nih.go
National Institute of Dental and Craniofacial Research (NIDCR):
Orlando Lopez, Ph.D.
301-402-4243
orlando.lopez@nih.gov
Automatic Detection and Tracing of SARS-CoV-2
National Institute on Alcohol Abuse and Alcoholism (NIAAA):
Changhai Cui, Ph.D.
301-443-1678
Changhai.Cui@nih.gov
RADx-rad Data Coordinating Center
National Library of Medicine (NLM):
Valerie Florance, Ph.D.
301-496-4621
florancev@mail.nlm.nih.gov
