Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R41 Clinical Trial Not Allowed) | RFA ES 21 003

Opportunity Information: Apply for RFA ES 21 003

The opportunity titled "Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R41 Clinical Trial Not Allowed)" is a National Institutes of Health (NIH) Small Business Innovation Research (SBIR) solicitation focused on early-stage technology development. It was released as Funding Opportunity Announcement RFA-ES-21-003 under CFDA number 93.113, with an original closing date of March 29, 2021, and a creation date of December 10, 2020. The funding mechanism targets Phase I SBIR work (the announcement text references Phase I as R43), and it explicitly states that clinical trials are not allowed under this FOA.

At its core, the FOA is looking for small business concerns to build and demonstrate new or improved methodologies that use artificial intelligence and machine learning to move environmental health science forward. Rather than funding general AI research, the emphasis is on practical, promising computational approaches that can make environmental health research and decision-making more accurate, efficient, and informative. The expectation is that applicants will develop approaches that can be validated and eventually used by researchers, regulators, or other stakeholders who need better tools for understanding environmental hazards and risks.

The scientific aims revolve around several connected needs in environmental health. One major theme is improving toxicity prediction, meaning AI/ML approaches that can better estimate whether and how chemicals may cause harm, potentially reducing reliance on slower, more expensive, or less scalable testing methods. Another theme is chemical prioritization, where models help decide which substances should be tested first or more deeply, especially when thousands of chemicals may have limited experimental data available. The FOA also highlights identifying and filling data or knowledge gaps in toxicity assessment, which points to methods that can detect where information is missing, guide targeted data collection, integrate disparate datasets, or improve confidence and interpretability in predictions. Finally, it calls out the broader environmental health picture by encouraging tools that support a more complete understanding of human exposures, differences in susceptibility across people or populations, and links to adverse health outcomes. In practical terms, this could include modeling exposure patterns, integrating biomonitoring or environmental monitoring data, accounting for vulnerable populations, or connecting exposure and toxicity information to health endpoints.

Eligibility is limited to small businesses, consistent with SBIR rules. Foreign institutions and other non-domestic (non-U.S.) entities are not eligible to apply, and non-U.S. components of U.S. organizations are also not eligible. However, the announcement notes that "foreign components," as defined in the NIH Grants Policy Statement, may be allowed, which generally means a U.S. applicant might be permitted to include certain discrete elements of the project performed abroad if well-justified and compliant with NIH policy, even though the applicant organization itself must be an eligible U.S. small business.

Overall, this FOA is best read as a targeted SBIR Phase I call for prototype-level, proof-of-concept AI/ML methods that can be validated and that directly address environmental health science challenges such as toxicity prediction, chemical testing prioritization, gap analysis in toxicology data, and improved understanding of exposures and susceptibility that drive health outcomes.

• The National Institutes of Health in the environment, health sector is offering a public funding opportunity titled "Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R41 Clinical Trial Not Allowed)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.113.
• This funding opportunity was created on 2020-12-10.
• Applicants must submit their applications by 2021-03-29. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: Small businesses.

Apply for RFA ES 21 003

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