NIH revises grant review process to try to reduce possible reputational bias

The National Institutes of Health (NIH) announced last week that it is adopting modified criteria in its grant review process beginning on January 25, 2024. The new system will continue to focus on the scientific merit of proposals (i.e., importance, rigor, and feasibility), while de-emphasizing criteria that may introduce bias into the review. New grant applications will be evaluated for whether the applicant demonstrates sufficient expertise and resources, but without considering the reputation of the institution or the investigator.

NIH announces five new Research Evaluation and Commercialization (REACH) Hubs

NIH recently announced awards for five Research Evaluation and Commercialization Hubs (REACH) to accelerate the creation of small businesses and the transition of academic research discoveries into products that improve patient care and enhance health. These new REACH hubs will support innovators from diverse personal, educational, and professional backgrounds across 76 non-profit research institutions spanning 12 states.

NIH puts the kibosh on generative AI

Last month, NIH came out with a policy statement that prohibits using generative AI to analyze or critique NIH grant applications and contract proposals. Specifically, as written in NIH Notice NOT-OD-23-149, “NIH prohibits NIH scientific peer reviewers from using natural language processors, large language models, or other generative Artificial Intelligence (AI) technologies for analyzing and formulating peer review critiques for grant applications and R&D contract proposals.” The problem with using generative AI in peer review is that it compromises confidentiality. As expressed in the notice, once information is loaded onto a generative AI platform, “AI tools have no guarantee of where data are being sent, saved, viewed, or used in the future, and thus NIH is revising its Confidentiality Agreements for Peer Reviewers to clarify that reviewers are prohibited from using AI tools in analyzing and critiquing NIH grant applications and R&D contract proposals. Such actions violate NIH’s peer review confidentiality requirements.”

Two webinars offer help to compete for NIH’s Research Evaluation and Commercialization Hubs (REACH)

NIH just launched the funding opportunity announcement for the third cohort of its regional biomedical proof-of-concept and accelerator program, the Research Evaluation and Commercialization Hubs. On Dec. 12, NIH will be hosting a funding opportunity announcement pre-application webinar which will provide a question and answer opportunity with NIH SEED and NIGMS.

Would an increase in the quantity of NIH SBIR awards impact their overall quality?

In a recent study titled Does NIH select the right healthcare ventures through the SBIR grant program?, researchers from Rutgers University and the University of Connecticut took advantage of the 2009 American Recovery and Reinvestment Act (ARRA) to conduct a natural experiment. The opportunity was available due to the National Institutes of Health (NIH) decision to use ARRA dollars to fund additional Phase I SBIR awards from general SBIR competitions, and the researchers compared these 19 ARRA-funded awards to the other 479 Phase I awards that were first funded in the same competitions with regular appropriations.

Recent Research: Access to information is key to SBIR effectiveness

Accelerators, incubators and entrepreneurial assistance programs work to ensure their startups understand their product’s market competition, customers, and supply chain. As it turns out, that’s also good advice for small research-based firms trying to move from SBIR proof-of-concept funding to securing the larger Phase II awards. A survey of approximately 250 National Institutes of Health (NIH) Small Business Innovation Research (SBIR) program awardees by researchers finds market information from suppliers, customers, and competitors to be key for small entrepreneurial firms to increase publicly funded research and development (R&D) effectiveness.

Useful Stats: NIH SBIR/STTR application success rates & trends, FY 2012-2021

In fiscal year 2021, the nationwide success rate of applicants for National Institutes of Health (NIH) Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Phase I awards decreased slightly from FY 2020. This continued a downward trend over recent years. The success rate for NIH SBIR/STTR Phase I was nearly 13 percent (647 of 5,132 approved) in FY 2021, a decrease from nearly 14 percent (636 of 4,684 approved) in FY 2020 and from nearly 16 percent for all proposals submitted over the past decade.

Pitch to secure ARPA-H headquarters location begins

With a $1 billion investment over the next three years, Advanced Research Projects Agency for Health (ARPA-H) will be a standalone agency within the National Institutes of Health (NIH) and is designed to produce quicker research outcomes. Published reports indicate multiple states are currently positioning themselves to host the headquarters of the new agency, with California, Georgia, Massachusetts, North Carolina, Ohio, Pennsylvania and Texas, having stated their interest in hosting the new headquarters.

Report: NIH SBIR/STTR program supported 99 drugs, numerous successful companies over 25 years

The Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs of the National Institutes of Health (NIH) supported the development of 99 drugs from 1996-2020 — a total that includes 16 percent of all such treatments that made a “significant” advance over available medicines. This finding is just one of the impacts that the National Academies of Sciences, Engineering and Medicine (NASEM) attributes to the program in a new report.

Feds seek input on manufacturing policy, scientific data

The National Science and Technology Council (NSTC) has released a new request for information (RFI) related to a national strategic plan for advanced manufacturing, and the National Institutes of Health (NIH) are seeking information on how the scientific community uses public data tools. Both RFIs provide an opportunity for the tech-based economic development field to shape the future of federal innovation policy. 


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