Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Data-to-Model: An Academic-Industrial Partnership (D2M-AIP), RFA-RM-27-012

Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Data-to-Model: An Academic-Industrial Partnership (D2M-AIP), RFA-RM-27-012 is sponsored by National Cancer Institute (NCI). This NCI funding opportunity supports research to advance the use of AI in cancer research, focusing on precision medicine by integrating imaging with multimodal data through academic-industrial partnerships.

RFA-RM-27-012: PRIMED-AI: Data-to-Model Academic-Industrial Partnerships (D2M-AIP) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (UG3/UH3 Clinical Trial Optional) Department of Health and Human Services Part 1.

Overview Information Participating Organization(s) National Institutes of Health ( NIH ) Components of Participating Organizations Office of Strategic Coordination ( Common Fund ) This Notice of Funding (NOFO) is developed as a Common Fund initiative ( https://commonfund. nih. gov/ ) through the Office of the NIH Director, Office of Strategic Coordination ( https://commonfund.

nih. gov/ ). All NIH Institutes and Centers participate in Common Fund initiatives.

The NOFO will be administered by the National Cancer Institute on behalf of the NIH. Note: Not all NIH Institutes, Centers, and Offices (ICOs) participate in Announcements. Applicants should carefully note which ICOs participate in this announcement and view their respective areas of research interest at the ICO-Specific Scientific Interests website .

ICOs that do not participate in this announcement will not consider applications for funding.

Funding Opportunity Title PRIMED-AI: Data-to-Model Academic-Industrial Partnerships (D2M-AIP) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (UG3/UH3 Clinical Trial Optional) UG3 / UH3 Exploratory/Developmental Phased Award Cooperative Agreement Check for any recent Notices of NIH Policy Changes that may impact application requirements.

Funding Opportunity Number (FON) Companion Funding Opportunity Research Project (Cooperative Agreements) Phase 1 Exploratory/Developmental Cooperative Agreement/Exploratory/Developmental Cooperative Agreement Phase II Specialized Center (Cooperative Agreements) Resource-Related Research Project (Cooperative Agreements) See Part 2, Section III. 3. Additional Information on Eligibility.

Assistance Listing Number(s) Funding Opportunity Purpose The overarching goal of this notice of funding opportunity (NOFO) and its companion opportunities is to establish the Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program to support development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools.

PRIMED-AI CDS tools are based on the integration of clinical imaging with other types of multimodal health data to enhance care for patients with a wide range of health conditions. The PRIMED-AI Program seeks to catalyze the adoption of AI-based CDS tools into clinical workflows to enable novel personalized medicine strategies that address significant health challenges.

The purpose of this Notice of Funding Opportunity (NOFO) is to catalyze the development and testing of Artificial Intelligence (AI)-enabled, image-centered, multimodal Clinical Decision Support (CDS) tools, developed in pursuance as Software as a Medical Device (SaMD). These projects are expected to have high potential for demonstrable, positive impact on patient outcomes and/or healthcare processes.

Funding Opportunity Goal(s) The Office of Strategic Coordination ( Common Fund ) supports research and other projects that will accelerate fundamental biomedical discovery and translation of that knowledge into effective prevention strategies and new treatments.

Open Date (Earliest Submission Date) Renewal / Resubmission / Revision (as allowed) AIDS - New/Renewal/Resubmission/Revision, as allowed All applications are due by 5:00 PM local time of applicant organization. Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

No late applications will be accepted for this Notice of Funding Opportunity (NOFO). Required Application Instructions It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide , except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts ).

Conformance to all requirements (both in the Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the Application Guide, follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review. There are several options available to submit your application through Grants. gov to NIH and Department of Health and Human Services partners.

You must use one of these submission options to access the application forms for this opportunity. Use the NIH ASSIST system to prepare, submit and track your application online. Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.

gov and eRA Commons to track your application. Check with your institutional officials regarding availability. Use Grants.

gov Workspace to prepare and submit your application and eRA Commons to track your application. Part 1. Overview Information Part 2.

Full Text of Announcement Section I. Notice of Funding Opportunity Description Section II. Award Information Section III.

Eligibility Information Section IV. Application and Submission Information Section V. Application Review Information Section VI.

Award Administration Information Section VII. Agency Contacts Section VIII. Other Information Part 2.

Full Text of Announcement Section I. Notice of Funding Opportunity Description Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) NOFOs seek to spur on the development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools with the potential for transformational impact.

The PRIMED-AI Program is based on the integration of clinical imaging with other types of multimodal health data that form the basis for CDS tool development and testing, which serve to enhance patient care for a wide range of health conditions.

The purpose of the Data-to-Model Academic-Industrial Partnership (D2M-AIP) NOFO is to support multi-sector and multi-disciplinary research teams, including investigators from both academia and industry, to create mutually beneficial opportunities for partners in the pre-competitive development stage.

D2M-AIP projects are primarily focused on the integration and harmonization of novel multiscale, multimodal data with clinical imaging data and the development and testing of truly novel AI-enabled, image-centered, multimodal CDS tools, developed in pursuance as Software as a Medical Device (SaMD).

D2M-AIP projects will leverage existing resources across the partnership, such as high-performance computing capabilities and access to clinical data, to generate robust validation data and engage with regulators, positioning the technology for rapid post-award translation into a viable and impactful clinical product. Key Terms used in PRIMED-AI Program Clinical decision support (CDS) tool.

A type of software, computational model, or digital system that is incorporated into clinical workflows to assist in determining a course of action related to patient care. Clinical imaging.

Any FDA-approved imaging modality used in patient care, including radiologic ( e.g., radiographic, computed tomographic, magnetic resonance, molecular, radionuclide imaging), ophthalmologic ( e.g., Optical Coherence Tomography), endoscopic, and dermatologic imaging, and video.

Clinical imaging of human participants is intended to be the anchor data type that multimodal data are integrated within the PRIMED-AI Program, which will form the basis for AI algorithm development and testing of CDS tools. DICOM standard. Digital Imaging and Communications in Medicine (DICOM) standard, the most widely used by the community to address interoperability challenge, is strongly encouraged but not required.

Inclusion of non-DICOM standard clinical imaging must include a plan to develop standards in conjunction with the PRIMED-AI community if none currently exist. Harmonization . The process of bringing together data from different sources and ensuring that it is consistent, comparable, and compatible.

This involves standardizing data formats, structures, and definitions so that data from various sources can be integrated and analyzed together effectively. Interoperability. The ability for AI models and associated data and metadata to be understood and work across different AI platforms and have the potential to be used consistently across different health systems.

Multimodal data (MMD). Representing different types of data and information from multiple sources that may include multiple clinical imaging modalities and non-imaging health data (e.g., electronic health records, EEG, EKG, laboratory test results (-omics), wearable sensor data, medical reports).

Multiscale data are encouraged; however, microscopy-based imaging of biospecimens ex vivo ( e.g., digital pathology) cannot represent the sole imaging data type. Although non-human imaging and/or MMD data may have assisted in development of an AI-model, overt representation and reliance on data derived from non-human sources for CDS tool development, testing, and validation will be given low programmatic priority. Playbook.

A collection of actionable guidelines, standardized protocols, and/or standardized operating procedures for the reliable and effective development and deployment of multimodal clinical decision support tools. The Playbook is a collection of frameworks. Precision Medicine.

Sometimes called personalized medicine or individualized medicine, refers to a healthcare approach that uses information based on a patient's individual characteristics such as health measures, genotype, phenotype, environment, and lifestyle information to guide, tailor, and optimize decisions related to their medical care and management. PRIMED-AI Consortium. The consortium constitutes members of PRIMED-AI excluding NIH program staff.

PRIMED-AI Program is an umbrella term encompassing the consortium, NIH staff, and overall programmatic objectives. Uncertainty Quantification. Measuring or quantifying the impact of uncertainties in complex systems, including quantifying the confidence in outcomes predicted by multimodal AI models.

Validation. Validation exists on a continuum in the PRIMED-AI Program. Analytical or technical validation is based on the evaluation of algorithmic performance and the ability of a multimodal AI model to make accurate predictions.

Initially, a model or algorithm can meet expected performance on retrospective and/or entirely new clinical datasets within the confines of a specific hospital or healthcare system. It is useful locally (internally) but is not yet applicable (generalizable) to the wider real-world population.

Subsequently, for clinical validation, a model or algorithm can be tested (externally) on new wider real-world population datasets to predict a meaningful outcome and meet regulatory criteria for the claimed use case. The PRIMED-AI Program anticipates validation of projects along this continuum as outlined in the NOFOs. Verification.

The process by which data integrity and construction of models is assessed for appropriateness within the context of use or intended purpose. The process by which data integrity and model is appropriate for the context of use and intended purpose.

Clinical imaging plays a pivotal role in disease diagnosis, treatment planning and monitoring, and assessment of health and treatment outcomes; however, current developments of artificial intelligence (AI) for clinical imaging-based clinical decision support (CDS) tools typically leverage data of a single imaging modality from radiological or ophthalmological sources, while health is shaped by a variety of interconnected factors–clinical, biological, genetic, environmental, and social.

The Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program seeks to integrate clinical imaging with relevant, complementary multimodal data (MMD). The overarching goal of the PRIMED-AI Program is to catalyze the development and adoption of innovative AI-based CDS tools into clinical workflows.

The PRIMED-AI Program initiatives collectively aim to tackle complex clinical challenges by fostering cross-disciplinary collaboration to create innovative, reliable, cost-effective, and sustainable AI solutions that enable new precision medicine strategies and improve patient outcomes.

Prior to applying, applicants are encouraged to check the PRIMED-AI Program website for updates to relevant FAQs and informational webinars, and are also encouraged to read all companion NOFOs to ensure they are aware of the goals and responsibilities of all PRIMED-AI award recipients, including methods PRIMED-AI intends to utilize to address error mitigation and technical management.

Familiarity with the companion NOFOs may better inform proposed D2M-AIP interconnections with other aspects of the PRIMED-AI Program.

D2M-AIP PRIMED-AI CDS tools aim to enhance diagnostic accuracy, optimize treatment, improve prognostic prediction, and streamline workflows for clinical decision-making through processing and integration of complex, multimodal data streams to ultimately improve patient outcomes and/or lower patient care costs.

This initiative and its companion Model-to-Clinic (M2C), directly address a critical translational gap in AI research–the gap between promising AI prototypes developed in controlled research environments and robustly validated, clinically deployable PRIMED-AI CDS tools that function reliably in real-world healthcare settings.

For the purpose of this NOFO, applicants must demonstrate sufficient access to verified, comprehensive, and complex multimodal datasets as well as integration and harmonization of these data to address a target clinical application. Applicants to the D2M-AIP NOFO will produce a "validated AI prototype," defined as a model that has undergone sufficient technical validation to demonstrate robust performance and technical feasibility.

Projects must move beyond single-site studies to ensure broad clinical applicability through comprehensive analytic validation (required) and clinical validation (where appropriate). Ultimately, the D2M-AIP NOFO aims to catalyze a new generation of AI-augmented healthcare delivery approaches that enhance diagnosis, prognosis, and/or treatment within a precision medicine framework.

The value of any PRIMED-AI CDS tool developed under this program is contingent upon its relevance, reliability, and broad clinical adoption to improve patient care and reduce patient care costs. Distinction Between Data-to-Model Academic-Industrial Partnership ( D2M-AIP ) and Model-to-Clinic ( M2C ) NOFOs The PRIMED-AI Program includes two parallel NOFOs designed with distinct primary foci along the data-to-model-to-clinic continuum.

To help applicants select the appropriate funding opportunity, the essential differences are summarized here: D2M-AIP projects aim toward commercialization and are led by an academic-industrial partnership that primarily focuses on novel data integration and/or new AI model development.

Projects proposed under D2M-AIP exist in the pre-competitive space and will emphasize technical validation and performance testing, without requiring clinical validation studies within the award period. The goal is to develop and de-risk novel AI technologies through collaboration, preparing them for future commercialization, adoption, and use of a CDS tool(s).

M2C projects primarily focus on assessing clinical adoption and impact by translating a promising AI model into a clinical workflow. M2C projects must conduct clinical validation studies to evaluate real-world utility for adoption in clinical care. M2C projects begin with a promising AI model and aim to assess and validate its clinical performance, utility, and impact in real-world healthcare settings.

They should also address challenges related to adopting AI-assisted CDS tools in clinical workflows, including associated costs and feasibility. The D2M-AIP NOFO outlines a partnership structure designed to bridge gaps in knowledge and expertise by combining the strengths of academic, industry, and other investigators.

Each application should establish an interdisciplinary, multi-institutional research team that collaborates strategically to develop and translate a solution to a defined problem. Teams are expected to plan, design, and validate the solution to ensure it meets the needs of end users. At a minimum, each partnership must include one academic and one industry organization.

This NOFO particularly encourages applications that focus on enhancing commercialization potential and facilitating the translation of AI innovations into commercially viable products. These teams have the potential to create mutually beneficial opportunities for partners in the pre-competitive development stage.

Partnerships are expected to synergistically leverage their existing resources such as high-performance computing capabilities and access to clinical data to create added value. While the development of a market-ready product is not required, projects must significantly reduce risks to enable future commercialization.

A primary goal of D2M-AIPs is to leverage the partnership to generate robust validation data and engage with regulators, positioning the technology for rapid post-award translation into a viable and impactful clinical product. Proposals should take FDA guidance for CDS tools and AI-based SaMD into account ( https://www. fda.

gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software , https://www. fda. gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device ).

Examples of responsive D2M-AIP research projects include, but are not limited to: (Focus: Novel data integration, new AI model development, and de-risking for commercialization) Developing a novel federated AI framework to fuse ophthalmic OCT imaging with glycemic and psychiatric data for diabetic retinopathy prediction.

A pre-competitive AI platform for integrating clinical imaging and multi-omics data that bridges the gap between scales to make predictions about treatment in patients with comorbidities and de-risk drug-induced toxicity for personalized care decisions. Producing reusable datasets and benchmark tasks for broader scientific use, including integration with the Genesis Mission and development of CDS tools.

Creation and technical validation of a multiscale AI model integrating endoscopic video and digital pathology for early detection in gastrointestinal cancers. Development of CDS tools that perform data fusion, dynamic coupling, and dimensionality reduction in the longitudinal pairwise integration of clinical imaging, multimodal data, and outcome measures at the single patient level.

CDS tools in UH3 phase leverage cohorts formed in the UG3 phase to find relationships and patterns for personalized medicine strategies, predictive AI solutions to clinical needs, explainability, and missing data.

Development of novel digital twins CDS tools leveraging clinical imaging, genomic profiling, and real-world patient-level data ( e.g., lab tests, activity and sleep data from wearables, semi-structured electronic medical record data, and patient reported outcomes).

CDS tools in the UH3 phase enable personalized medicine for on-the-fly modeling of individual response trajectories to guide decisions about treatment, follow-up visits, and survivorship. D2M-AIP projects supported by this NOFO will involve two distinct, milestone-driven phases of innovation research and development.

In the UG3 phase, D2M-AIP award recipients will integrate comprehensive clinical imaging and multimodal data streams for AI model building and conduct initial pilot studies with a novel AI-enabled CDS tool.

In the UH3 phase, award recipients will refine, further develop, and systematically validate the performance of these tools, providing evidence of their potential for transformative impact on real-world challenges in precision medicine.

Applications must be built upon the following five pillars (further specified in Section IV): Data Quality and Governance for Model Development Clinically Grounded AI Technology Pathway to Implementation and Adoption Integrated Multidisciplinary Team Error Mitigation and Technical Management Applications Not Responsive to this NOFO. To be considered responsive, applications must align with the central goal of the PRIMED-AI Program.

The following types of applications will be considered non-responsive and will not be reviewed: Projects primarily focused on basic research in AI/ML methodology without a clear and significant translational goal towards a specific clinical problem. Projects that do not focus on the translation of a CDS tool that uses clinical imaging as the anchor data type, integrated with other multimodal data.

Clinical imaging must be the modality that anchors the model. Digital pathology cannot represent the imaging anchor data type but may serve as a form of multimodal data. Projects where the primary focus is on a biological research question and the AI technology and methods are already well-established, adapted, optimized, and validated for that context of use.

Applications proposing Phase III clinical trials as the primary scope of work. (Note: While this NOFO is "Clinical Trial Optional," large-scale, confirmatory Phase III trials are outside the scope). Projects that do not propose the development or validation of an AI-powered, image-centered, multimodal PRIMED-AI CDS tool.

Projects that do not define a targeted unmet clinical problem (intended use) on a clinical population (intended users) for the proposed CDS tool. Applications that do not include substantive academic-industrial partnership(s) as described in Section IV. Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.

See Section VIII. Other Information for award authorities and regulations. Section II.

Award Information Cooperative Agreement: A financial assistance mechanism used when there will be substantial Federal scientific or programmatic involvement. Substantial involvement means that, after award, NIH scientific or program staff will assist, guide, coordinate, or participate in project activities. See Section VI.

2 for additional information about the substantial involvement for this NOFO. Application Types Allowed The OER Glossary and the How to Apply Application Guide provide details on these application types. Only those application types listed here are allowed for this NOFO.

Optional: Accepting applications that either propose or do not propose clinical trial(s). Need help determining whether you are doing a clinical trial? Funds Available and Anticipated Number of Awards The NIH Common Fund intends to commit funds for approximately 6-8 UG3/UH3 awards.

The number of awards is contingent upon NIH appropriations and the submission of a sufficient number of meritorious applications. Applicants should request a budget appropriate for the proposed scope of work, not to exceed $450,000 in direct costs per year for UG3 and $800,000 for UH3 phases. The total project period for a UG3/UH3 award may not exceed 5 years.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO. Section III.

Eligibility Information Higher Education Institutions - Includes all types Public/State Controlled Institutions of Higher Education Private Institutions of Higher Education Nonprofits Other Than Institutions of Higher Education Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education) Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education) For-Profit Organizations (Other than Small Businesses) City or Township Governments Special District Governments Indian/Native American Tribal Governments (Federally Recognized) Indian/Native American Tribal Governments (Other than Federally Recognized).

Eligible Agencies of the Federal Government U.S. Territory or Possession Independent School Districts Public Housing Authorities/Indian Housing Authorities Native American Tribal Organizations (other than Federally recognized tribal governments) Faith-based or Community-based Organizations Non-domestic (non-U.S.) Entities (Foreign Organizations) Foreign Organizations/International Collaborations Non-domestic (non-U.S.) Entities (Foreign Organizations) 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. NIH will no longer issue awards (i.e., new, renewal, or non-competing continuation) to domestic or foreign entities that involve foreign subawards/subcontracts.

All NIH-funded research involving foreign subawards/subcontracts must be submitted in response to a NOFO that is specifically designated for funded international collaborations. See NIH Grants Policy Statement 16. 8 Collaborative International Research Awards .

Applications involving foreign subawards/subcontracts submitted in response to this NOFO will be deemed noncompliant and will not be considered for funding.

This policy applies to all monetary international collaborations resulting in foreign subawards/subcontracts, however, it does not preclude unfunded international collaborations or foreign components , funding for foreign consultants, or procurement of unique equipment or supplies from foreign vendors.

Applicant organizations must complete and maintain the following registrations as described in the How to Apply- Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible.

Failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference the NIH Grants Policy Statement Section 2. 3. 9.

2 Electronically Submitted Applications for additional information. System for Award Management (SAM) – Applicants must complete and maintain an active registration, which requires renewal at least annually . The renewal process may require as much time as the initial registration.

SAM registration includes the assignment of a Commercial and Government Entity (CAGE) Code for domestic organizations which have not already been assigned a CAGE Code. Foreign organizations must obtain a NATO Commercial and Government Entity (NCAGE) Code (in lieu of a CAGE code) in order to register in SAM. Unique Entity Identifier (UEI)- A UEI is issued as part of the SAM.

gov registration process. The same UEI must be used for all registrations, as well as on the grant application. eRA Commons - Once the unique organization identifier is established, organizations can register with eRA Commons in tandem with completing their Grants.

gov registrations; all registrations must be in place by time of submission. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application. Grants.

gov – Applicants must have an active SAM registration in order to complete the Grants. gov registration. Program Directors/Principal Investigators (PD(s)/PI(s)) All PD(s)/PI(s) must have an eRA Commons account.

PD(s)/PI(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in eRA Commons. If the PD/PI is also the organizational Signing Official, they must have two distinct eRA Commons accounts, one for each role. Obtaining an eRA Commons account can take up to 2 weeks.

All PD(s)/PI(s) must be registered with ORCID . The personal profile associated with the PD(s)/PI(s) eRA Commons account must be linked to a valid ORCID ID. For more information on linking an ORCID ID to an eRA Commons personal profile see the ORCID topic in our eRA Commons online help .

Eligible Individuals (Program Director/Principal Investigator) Any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the Program Director(s)/Principal Investigator(s) (PD(s)/PI(s)) is invited to work with their organization to develop an application for support.

For institutions/organizations proposing multiple PDs/PIs, visit the Multiple Program Director/Principal Investigator Policy and submission details in the Senior/Key Person Profile (Expanded) Component of the How to Apply-Application Guide.

Investigators from U.S. Federal Government Agencies (see eligibility statement above) may participate in this program as unpaid collaborators, co-Is, or unpaid consultants in accord with the Terms and Conditions provided in this NOFO. While their expertise and resources are highly valued, NIH intramural scientists cannot receive salary support or any other direct financial compensation from funds awarded through this extramural NOFO.

Their involvement should be clearly outlined in the application, including a description of their scientific contribution, the number of person months devoted to the project, and a formal letter of collaboration from their Institute/Center Scientific Director or equivalent, confirming their commitment to the project and that no grant funds will be used for their support or the operational costs of NIH intramural facilities.

Any use of NIH intramural resources should be fully justified and approved by the relevant NIH Institute/Center. The grant applicant is responsible for writing the section of the grant that describes the proposed collaboration within the grant, which the NIH investigator should see and approve.

Intellectual property will be managed in accord with established policy of the NIH in compliance with Executive Order 10096, as amended, 45 CFR Part 7; patent rights for inventions developed in NIH facilities are NIH property unless NIH waives its rights.

For institutions/organizations proposing multiple PDs/PIs, visit the Multiple Program Director/Principal Investigator Policy and submission details in the Senior/Key Person Profile (Expanded) Component of the How to Apply-Application Guide.

Specific Eligibility for Federally Funded Research and Development Centers (FFRDCs) and University Affiliated Research Centers (UARCs): FFRDCs and UARCs may not apply to this Research Opportunity Announcement as a prime performer/lead institution; however, subject to any restrictions (i.e., direct competition limitations as determined by the entity or potential limiting organizational conflicts of interest, agency sponsor related requirements and policies, etc.), FFRDCs and UARCs may be included as part the prime performer's proposal, as a sub-awardee.

As with all prime/sub-awardee teaming arrangements, the Government will only have privity of contract with the prime performer, and all payments will be made through the prime awardee. Will require strong justification U.S. collaborators encouraged Subawards for foreign investigators are not allowed Unpaid foreign collaborators are allowed This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement Section 1.

2 Definition of Terms . 3. Additional Information on Eligibility Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time, per NIH Grants Policy Statement Section 2. 3. 7.

4 Submission of Resubmission Application . This means that the NIH will not accept: A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application. A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.

An application that has substantial overlap with another application pending appeal of initial peer review (see NIH Grants Policy Statement 2. 3. 9.

4 Similar, Essentially Identical, or Identical Applications ). Section IV. Application and Submission Information 1.

Requesting an Application Package The application forms package specific to this opportunity must be accessed through ASSIST, Grants. gov Workspace or an institutional system-to-system solution. Links to apply using ASSIST or Grants.

gov Workspace are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution. 2.

Content and Form of Application Submission It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide except where instructed in this notice of funding opportunity to do otherwise (in this NOFO, in a policy notice , or other notice from NIH Guide for Grants and Contracts ). Conformance to the requirements in the Application Guide is required and strictly enforced.

Applications that are out of compliance with these instructions may be delayed or not accepted for review. All page limitations described in the How to Apply- Application Guide and the Table of Page Limits must be followed. Instructions for Application Submission The following section supplements the instructions found in the How to Apply- Application Guide and should be used for preparing an application to this NOFO.

All instructions in the How to Apply - Application Guide must be followed. SF424(R&R) Project/Performance Site Locations All instructions in the How to Apply- Application Guide must be followed. SF424(R&R) Other Project Information All instructions in the How to Apply- Application Guide must be followed.

SF424(R&R) Senior/Key Person Profile All instructions in the How to Apply- Application Guide must be followed. All instructions in the How to Apply- Application Guide must be followed. All instructions in the How to Apply- Application Guide must be followed.

The budget request for this NOFO must distinguish between extramural costs, and the NIH intramural investigator costs. Extramural costs are associated with the extramural investigator and the applicant organization. NIH intramural investigator costs are those required by the intramural investigator for carrying out the proposed work and which are specifically identified with the project.

Cost of NIH Intramural work Please note that the NIH Intramural Research program (IRP) costs and participation will not be included in the award paid to the grantee. However, cost of IRP reagents, consumables and data analysis (e.g. software licenses) should be submitted as a separate budget page in the budget section.

IRP budgets may not include any salary and related fringe benefits for career, career conditional or other federal employees (civilian or uniformed service) with permanent appointments under existing position ceilings or any costs related to administrative or facilities support (equivalent to Facilities and Administrative costs).

These costs may include salary for staff to be specifically hired under a temporary appointment for the project. The IRP budget may also include consultant costs, equipment, supplies, travel, and other items typically listed under other expenses. Funds can be requested for services by an external investigator or contractor as a subcontract/consortium including the applicable indirect (F&A) Extramural costs