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Emergency Awards RADx-RAD: Novel Biosensing for Screening, Diagnosis and Monitoring of COVID-19 From Skin and The Oral Cavity (Fast-Track STTR Clinical Trial Not Allowed)
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The official link for this solicitation is: https://grants.nih.gov/grants/guide/rfa-files/RFA-OD-20-021.html
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Emergency Awards RADx-RAD: Novel Biosensing for Screening, Diagnosis and Monitoring of COVID-19 From Skin and The Oral Cavity (Fast-Track STTR Clinical Trial Not Allowed)
NIH is issuing this funding opportunity announcement (FOA) in response to the declared public health emergency issued by the Secretary, HHS, for 2019 Novel Coronavirus (COVID-19). This emergency FOA from the National Institutes of Health (NIH) provides an expedited funding mechanism as part of the Rapid Acceleration of Diagnostics-Radical (RADx-rad) initiative. The goal of this RFA is to solicit Fast-Track STTR applications to advance development of novel, non-traditional, safe and effective biosensing and detection approaches to identify the current SARS-CoV-2 virus or other biomarkers of the COVID-19 disease for use in outbreaks of COVID-19, as well as for use in future pandemics resulting from unknown viruses. The funding for this award is provided from the Paycheck Protection Program and Health Care Enhancement Act, 2020. BACKGROUND SARS-CoV-2 is a novel coronavirus that has recently been identified as the causative agent of COVID-19, a respiratory disease that exhibits a wide range of clinical outcomes from asymptomatic and mild disease to severe viral pneumonia, Acute Respiratory Distress Syndrome (ARDS), Multisystem Inflammatory Syndrome in Children (MIS-C), acute kidney injury, thrombotic disorders, and serious cardiac, cerebrovascular and vascular complications. On March 11, the SARS-CoV-2 outbreak was classified as a pandemic by the WHO. Research is an important component of the public health emergency response before, during and after the emergency. The United States Food and Drug Administration (FDA)-authorized COVID-19 diagnostic testing is critical for slowing the spread of the virus and preventing future outbreaks. Thus, there is an urgent public health need for the National Institutes of Health (NIH) to support the development of a variety of approaches to testing. Expanding the capacity, throughput, and regional placement of existing technologies and accelerating the development of new technologies will contribute significantly to the current national efforts to curb the COVID-19 pandemic. To help meet this need, NIH launched the Rapid Acceleration of Diagnostics (RADx) initiative to speed innovation in the development, commercialization, and implementation of technologies for COVID-19 testing. 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. As part of this program, the NIH developed the RADx Radical (RADx-rad) initiative. RADx-rad will support new, or non-traditional applications of existing approaches, to enhance their usability, accessibility, and/or accuracy. RADx-rad 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. Beyond the current crisis, it is anticipated that the technologies advanced through RADx-rad may also be applicable to other, yet unknown, infectious agents. To centrally align and coordinate RADx-rad projects to harmonize the data collection, storage, and management, the Data Coordination Center (DCC) will be established to serve as the “hub” in a hub-and spoke organizational framework within the funded RADx-rad research and development projects serving as spokes. In turn, the DCC will serve as a spoke in the larger NIH RADx initiative by providing de-identified data to an NIH-based data hub. NIH expects that all projects funded under this FOA will actively coordinate, collaborate, and share data with the RADx-rad Data Coordinating Center, as allowed, and with considerations under tribal IRB processes, as appropriate. The RADx-rad DCC will provide support and guidance to RADx-rad awardees in the following three areas: (1) Administrative Operations and Logistics, (2) Data Collection, Integration and Sharing, and (3) Data Management and Use. The DCC will develop (and revise as necessary) a framework for standards, metadata and common data elements that apply to all types of data gathered by RADx-rad awardees in order to maximize potential for longitudinal research, integration with other RADx data, and for evaluation of RADx-rad program impact. The DCC will assist awardees in identifying and obtaining data from public sources (e.g., Census data, Area Deprivation Index, etc.), electronic health records (EHR), administrative data, and others as needed. The DCC will coordinate quality control, data curation, and analyses, and provide tools to monitor progress, performance, and use of the curated data. The DCC will create a mechanism to support harmonizing with other large-scale COVID-19 research efforts and will participate in trans-NIH efforts to support scientific collaboration and data-sharing, evaluation of progress towards sustainable infrastructure, partnership and rapid dissemination of RADx findings. NIH requires that all projects funded under this RFA will actively coordinate, collaborate, and share data with the RADx-rad Data Coordinating Center, as allowed, and with considerations under tribal IRB processes, as appropriate. Researchers applying to this funding opportunity are strongly encouraged to review the Data Coordinating Center (DCC) funding opportunity (RFA-OD-20-019). To the extent possible, data acquisition, collection, and curation strategies should be coordinated with the DCC guidance for annotation and benchmarking of data, including obtaining appropriate consent for data sharing and implementation of the schemas proposed under the ABOUT ML effort (“Annotation and benchmarking on understanding and transparency for machine learning lifecycles”; available at https://www.partnershiponai.org/about-ml/). In order to maximize progress and successful outcomes, recipients are expected to participate in DCC-organized activities, including regular (e.g., monthly) progress meetings with individual or subsets of awardees, and twice annual meetings with all RADx-rad awardees. To maximize research and rapidly implement approaches to address the COVID-19 pandemic, comparisons across datasets or studies and data integration are essential to collaboration. Projects funded through this RFA are strongly encouraged to use the following resources as applicable: Data Harmonization for Social Determinants of Health via the PhenX Toolkit: Investigators involved in human-subject studies are strongly encouraged to employ a common set of tools and resources that will promote the collection of comparable data on social determinants of health (SDOH) across studies. In particular, studies with human participants should incorporate SDOH measures from the Core and Specialty collections that are available in the Social Determinants of Health Collection of the PhenX Toolkit (www.phenxtoolkit.org). A trans-NIH working group is making existing COVID-19 survey items and investigator contact information publicly available through two NIH-supported platforms: the NIH Public Health Emergency and Disaster Research Response (DR2) [https://dr2.nlm.nih.gov/] and the PhenX Toolkit [https://www.phenxtoolkit.org/index.php]. Researchers addressing COVID-19 questions, whether population-based or for clinical research, are strongly encouraged to consider these COVID-19 specific survey item repositories and select existing survey items or protocol modules currently being fielded. PURPOSE This RFA solicits Fast-Track STTR applications to advance development of novel, non-traditional, safe and effective biosensing and detection approaches to identify the current SARS-CoV-2 virus or biomarkers of the COVID-19 disease, and/or with potential to address other pandemics from unknown viruses. Biosensing and detection technologies submitted to this initiative should provide associations between biomarkers emanating from skin or the oral cavity to patients with symptomatic and asymptomatic COVID-19. Leveraging the accessibility of human skin and the oral cavity, this FOA seeks 1) to advance novel biosensing technologies that are innovative, safe, and effective, and 2) to implement such technologies into devices with integrated artificial intelligent (AI) systems for the detection, diagnosis, prediction, prognosis and monitoring of COVID-19 in clinical, community and everyday settings. Biosensing devices are expected to target skin or the oral cavity as sampling sites. Skin biosensing designs must target detection of volatile organic compounds (VOCs, i.e. scents or odors) emanating from skin in passive and noninvasive manner for use at point of care. In addition to VOCs, oral biosensing technologies may target a wealth of biological, chemical and physical biosignatures representative of SARS-CoV-2 virus and/or COVID-19 disease sampled from exhaled breath/droplets, saliva, and tissues in the oral cavity using a variety of detection schemes. To this end, leveraging dedicated engineering and artificial intelligence systems is required. For skin monitoring, the device can include Electronic-nose (E-nose) technology or Gas Chromatography (GC). Thus, biosensing technologies targeting VOCs emanating from skin or the oral cavity will be referred to as SCENT (Screening for COVID-19 by E-Nose Technology). Oral biosensing devices may consist of technologies that are thoroughly characterized as safe and effective in preclinical studies to conform to and perform in the oral cavity. Non-invasive, real-time, continuous or periodic measurements of VOCs and other biomarkers in breath, droplets, tissues and other samples emanating from the oral cavity as signatures of onset, progression, and resolution of COVID-19 are desirable. This FOA expects multidisciplinary collaborations to ensure project success. Disciplines may include: Biomedical engineers, material scientists, biosensing experts, software engineers, chemists, dentists, clinicians, virologists, clinical trialists, biostatisticians, data analysts and other relevant experts in academia and industry. SPECIFIC RESEARCH OBJECTIVES SCENT and Oral Cavity Biosensing: With the tentative opening of many States came an increase in COVID-19 cases, thus, 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. It is highly desirable for an accurate and sensitive system that can provide results in real time and is mobile/portable and deployable in any clinical, community and everyday setting. 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: 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 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 SCENT and Oral Biosensing Technologies Product Development Plans: Product development plans should conform to an aggressive RADx-rad program timeline without sacrificing expected performance milestones on safety or effectiveness. Adoption of Rapid Prototyping, Agile Methodologies and Quality by Design (QbD) principles are highly encouraged to advance projects with demonstrated early stage feasibility to late stage preclinical development of SCENT and oral biosensing platforms towards relevant FDA certifications/approvals. A holistic strategy that accounts for end-state manufacturing, production, and usability is expected. Early identification of critical product attributes (e.g., comparable accuracy with the current standard, safety, portability, etc.) and process parameters (e.g., ruggedness of materials used in the device, but also built-in process analytical technologies to ensure acceptable device function) are required. Design of experiment (DoE) techniques are essential. These preclinical development strategies will increase the likelihood of success in meeting eventual clinical performance requirements. It is envisioned that these technologies will complement traditional virus and antibody detection to monitor the onset, progression, and resolution of COVID-19. Applicants are strongly encouraged to use a systems approach to product development and preclinical performance testing of the proposed device to establish a robust proof-of-concept feasibility towards possible FDA certification/approval and/or product commercialization. Assembly and integration of the prototype SCENT and Oral Biosensing platforms: The SCENT platform should comprise (1) a Volatile Organic Compound (VOC) sampler; (2A) an electronic nose or (2B) a gas chromatographic (GC) column; (3) an appropriate detector, (4) AI/Machine Learning capabilities to distinguish VOC skin signatures across many levels of COVID-19 infection for accurate diagnosis, and (5) an intuitive, user-friendly interface. Assembly of the hardware and software for either the E-nose or GC prototypes from off-the-shelf components is encouraged to expedite development. The VOC skin sampler can be a simple cup where the VOCs emanating from skin are delivered to the e-Nose or GC column via nitrogen gas or other nonreactive gas, or it can be slightly more sophisticated as in solid-phase microextraction (SPME) where the gases are adsorbed on a material and desorbed into the e-Nose or GC column. The skin VOC sampling system will be developed through testing and optimization of potential designs preferably by Design of Experiments (DoE). The sampling system(s) will have to be validated against known mixtures of VOCs with and without in vitro skin models. Other novel skin sampling designs are encouraged. For the oral cavity sampler, the oral environment presents specific design and performance requirements for VOC sampling that need to be addressed. Therefore, a system’s engineering approach should address major challenges imposed by the oral environment including, varying pH levels and temperatures, oral flora, adhesion to wet intraoral tissues, and material biocompatibility/biofouling. The sampler for VOCs in the oral cavity can be a blow tube for breath and/or particulates/droplets. However, prevention of infection/contamination of the healthcare worker must be built-in into the design. The sampler can also be an oral probe (analogous to an oral thermometer) placed in contact with oral tissues (e.g., under the tongue) to collect VOCs emanating to the tissue surface. Alternatively, VOCs can be collected from the head space of saliva collected in a closed container. This is a standard procedure that has been applied to VOC analysis of urine. As in the skin sampler, other novel sampling designs for the oral cavity are encouraged. The E-Nose is generally an array of a number of materials including conducting polymers, quartz crystal microbalances, fluorescence sensors, semi-conducting metal oxides, etc. The collective signals are processed in an artificial neural network and pattern recognition software. For GC the detector can be a mass spectrometer (MS) or flame ionization detector (FID). Identification of the VOC components by comparison to an MS data base is desirable; however, the pattern recognition is more important. Deposition of data/learning sets into the DCC is required. The portable E-nose or GC instrumentation and detector/s will be integrated with the VOC sampler. For off-the-shelf components, compatibility of software should be considered even before assembly of the SCENT platform. The prototype has to be validated against the current standard, laboratory-grade, commercially-available GC or E-nose instrumentation (e.g., MEMS, etc.) and tested for sensitivity and accuracy against known mixtures of VOCs. Quality by Design (QbD) and incorporation of process analytical technologies (PATs) are required and must be prominently described in the application. QbD will allow for ease and precision of future manufacturability and ensure that device to device differences are at a minimum, while adverse events that are of device origin are limited. Process analytical technologies are required for accuracy and precision of measurements between devices and between patients. PATs for devices may include standard VOC mixtures, pressure sensors, etc. PATs for patient to patient standardization must include skin temperature sensors, skin permeability sensors (e.g., Transepidermal Water Loss [TEWL], skin impedance, etc.) or skin stiffness among others, that can affect the quantity (and perhaps quality) of VOCs sensed by the detectors. Adoption of human-factors-engineering and usability-engineering principles must be considered during the development process. This includes usability criteria such as comfort to ensure user acceptance and compliance. Additionally, VOC sensing approaches for COVID-19 must incorporate design specifications and performance criteria for risk mitigation of potential measurement interferents including, but not limited to: metabolites produced in pathological conditions other than COVID-19; compounds introduced during patient treatment such as drugs, plasma expanders, and anticoagulants; substances ingested by the patient such as alcohol or nutritional supplements. Lastly, the proposed approaches should address other potential causes of examination (analytical) interference, such as: chemical, physical and detection artifacts; non-selectivity and non-specificity of detection; and other sources of error that might affect COVID-19 diagnostics. Risk mitigation and alternative methods are expected. Most of the technological components required to build these sensing platforms are already developed and used for other purposes. The innovation in this initiative will be in bringing together the expertise and capabilities in these technologies and coupling them with clinical and infectious disease expertise to develop an integrative noninvasive device that will be used specifically for the diagnosis of COVID-19. Future applications of SCENT have potential for monitoring of overall health and detection of other diseases and will be a consideration but not required. The metrics/requirements for successful SCENT are accuracy, sensitivity and selectivity comparable to or exceed current standard, FDA-approved COVID-19 diagnostics. Portability, accessibility, and affordability are also key considerations for SCENT and oral biosensing. Since SCENT and oral biodevices can potentially have global applicability and use, it must follow the principles of ASSURED (affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable to end users) criteria, outlined by the World Health Organization (WHO), which provides a good framework for evaluating point of care devices specially for resource-limited environments. Software and Integrated Systems Development: Software systems and algorithms used in testing protocols from biomarker sampling (e.g., VOCs, biomolecules, antibodies, etc) to analysis will be developed and tested for smooth, error-free operation of the device. A robust performance testing plan must be established to ensure verification and validation of compatibility and integration between proposed biosensing technologies with other off-the-shelf hardware and software components sourced from different manufacturers at a unit and systems level. Use of commercially available pattern-recognition and machine learning software is allowed and even encouraged to facilitate agile and rapid development. The sensitivity and accuracy of the software must be verified and validated on surrogate samples. In addition, reference/training and validation sets from actual clinical samples will be used to show proof of principle of the machine learning algorithm. Testing SCENT and Oral Biosensing Prototypes: This initiative requires that the preclininical performance of proposed novel biosensing technologies is tested to ensure human safety and effectiveness according to recognized industry standards and relevant FDA regulatory considerations. Product development plans may include: preclinical performance testing utilizing benchtop evaluation, animal models and human tissue samples; design validation in actual or simulated use with test subjects; comparative experiments against gold standard molecular methods; usability evaluations with clinicians, patients and other end users; and secondary analysis of physiologic data and metadata from existing clinical studies. As appropriate, performance evaluations should consider relevant testing of individual biodevice components and integrated systems with respect to biocompatibility, biofouling, shelf-life, sterility assurance, mechanical and structural integrity, electromagnetic compatibility, electrical safety, usability/operability, wireless data transmission, battery safety and life-time, data security and privacy, and any other criteria needed to support the targeted clinical application. Requirements for rapid analysis are driven by the stability and selectivity of biomarkers for detection by biosensor systems. As appropriate, innovation of verifiable biomarkers, creative use of nanotechnology and other functionalization approaches should be considered to improve the analytical performance of biosensors for specific and sensitive detection characteristics needed for point-of-care diagnostics. Usability criteria such as comfort, discreetness, and absence of interference with daily functions must be considered to ensure user acceptance and compliance. For patient testing, data training sets must be collected on known (1) COVID-19 positive, symptomatic, (2) COVID-19 positive, asymptomatic and (3) COVID-19 negative subjects as determined by the current standard FDA approved method. A statistically significant number of patients with known condition(s) are expected to be tested for confident delineation of (1), (2) and (3) based on training sets and comparable accuracy to FDA-approved COVID-19 diagnostics. NCATS Clinical and Translational Science Awards (CTSA) hubs can be used in the clinical validation for recruitment and trial implementation. Evaluation Plan: Projects must include an evaluation plan demonstrating how the proposed COVID-19 diagnostic strategies/activities will be assessed safety, effectiveness and impact . Identification of appropriate risk mitigation strategies and alternative methods is expected. Since SCENT and oral biodevices can potentially have global applicability and use, the principles of ASSURED (affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable to end users) criteria, outlined by the World Health Organization (WHO), should be considered as framework for evaluating point of care devices specially for resource-limited environments. Leveraging Existing Research Resources: Applicants are also strongly encouraged to leverage existing research resources for their studies whenever possible. NIH has developed innovative solutions that will improve the efficiency, quality, and impact of the process for turning observations in the laboratory, clinic and community into interventions that improve the health of individuals and the public through programs such as: NCATS Clinical and Translational Science Awards (CTSA) Program, Research Evaluation and Commercialization Hubs (REACH), Small Business Education and Entrepreneurial Development (SEED), Commercialization Accelerator Program (CAP) for assistance in proof of concept and commercialization of a marketable product. Applicants are encouraged to leverage all available internal (e.g., home institutional) and external (e.g., external institutional, NIH, and/or NIDCR and NCATS) resources to identify clinically relevant COVID-19 patient populations. Regulatory Approval Plan: A plan for the regulatory approval of technologies, tests and approaches must be developed based on the data generated from the research objectives. The plan should be aligned with relevant regulatory requirements for the technology and describe foreseeable regulatory risks that could impact the technology development. The plan must also describe how the technology would fit with current standard of care. Milestone Plan: All projects will be milestone-driven towards late-stage preclinical development and potential commercialization of the proposed technologies based on clear go/no-go criteria that are quantifiable. Applicants must include a Milestone Plan. Prior to funding an application, the Program Official will contact the applicant to discuss the proposed project milestones and any changes suggested by NIH staff or the NIH review panel. The Program Official and the applicant will negotiate and agree on a final set of approved project milestones which will be specified in the Notice of Award. These milestones will be the basis for judging progress and the successful completion of the work proposed. See https://www.ninds.nih.gov/Funding/Apply-Funding/Application-Support-Library/Devices-Milestones for an example of milestones. NOTE: These are suggested formats only and should be adapted as appropriate See Section VIII. Other Information for award authorities and regulations.