Regulatory Compliance
All awards are subject to the comprehensive
NIH Grants Policy Statement and
2 CFR Part 200 (Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards). Recipients must adhere to all applicable non-discrimination laws and relevant federal statutes and regulations.
Data Protection and Privacy
A
Data Management and Sharing Plan is required for all applications involving the generation of scientific data. This plan outlines how data will be managed and shared, and its implementation is mandatory upon approval.
Ethical Standards
Projects involving
human subjects or
vertebrate animals must demonstrate adherence to strict ethical guidelines, including justifications for their involvement, protection against risks, and appropriate procedures for care and use. Evaluation also considers potential
biohazards and proposed protective measures.
Risk Management
Applications are expected to demonstrate
rigorous study design and transparent reporting to minimize bias, ensure reproducibility, and allow for accurate interpretation of results. This includes detailed plans for experimental design, controls, sample size justification, and statistical methods.
Unique Aspects and Strategic Opportunities
This grant utilizes a unique
R61/R33 phased innovation award mechanism. The R61 phase supports initial development and internal validation, while the R33 phase supports scaling and external validation. Progress from R61 to R33 is strictly contingent on the successful completion of
pre-defined, quantitative go/no-go milestones.
Key exclusions from this NOFO include:
- Studies involving therapeutic agent screening or efficacy/safety evaluation.
- Basic science studies focused on disease mechanisms (unless directly for face/construct validation based on accepted hypotheses).
- Development of diagnostic, monitoring, predictive, or prognostic biomarkers.
- Development of devices, device/drug combinations, surgical procedures, or rehabilitation strategies.
Strategic Alignment: Applicants are strongly encouraged to form
multidisciplinary teams that include academic experts, industry professionals, biostatisticians, and particularly
clinicians with an understanding of human conditions and therapeutic development. Such collaborations can significantly enhance the translational relevance and impact of the proposed model.