Opportunity Information: Apply for RFA ES 21 002
The grant opportunity titled "Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R43 Clinical Trial Not Allowed)" is a Phase I SBIR (Small Business Innovation Research) funding announcement from the National Institutes of Health (NIH), focused on helping small businesses create new AI- and machine learning-driven methods that can strengthen environmental health science. It is issued as a discretionary grant under Funding Opportunity Number RFA-ES-21-002 (CFDA 93.113) and is designed to support early-stage, high-potential technical development rather than clinical research involving human participants in clinical trial formats.
At its core, the FOA is looking for small business concerns to propose and build promising AI/ML methodologies that can improve how environmental health decisions and research are carried out. The emphasis is on method development that, once further developed and validated beyond Phase I, could meaningfully enhance toxicology and exposure science workflows. The announcement frames AI and ML as tools for making toxicity prediction more accurate, which can reduce uncertainty in hazard identification and risk-related decision-making. It also highlights a practical need in environmental health: the ability to prioritize chemicals for more relevant, efficient, or targeted testing, especially when resources do not allow comprehensive testing of all compounds at the same depth.
Another major goal is to address gaps in existing toxicity assessment data and knowledge. Many chemicals have incomplete datasets, inconsistent evidence across studies, or limited mechanistic understanding. The FOA encourages approaches that can identify where the gaps are, suggest what kinds of new data would be most valuable, and potentially help fill those gaps through predictive modeling or integration of diverse sources. In addition, the opportunity is interested in methods that advance understanding of how human exposures translate into health outcomes, including differences in susceptibility. That includes the idea that exposures do not affect all people equally, and that AI/ML could help disentangle how exposure patterns, biological variability, and other contributing factors relate to adverse health effects.
This is specifically a Phase I (R43) SBIR opportunity, meaning it is intended for feasibility, proof-of-concept work, and early technical validation rather than full-scale deployment. In practical terms, applicants would be expected to demonstrate that their AI/ML approach is technically plausible and shows enough promise to justify later-stage development, validation, and commercialization or broader use in environmental health contexts. The "Clinical Trial Not Allowed" designation signals that the proposed research should not include clinical trial activities as defined by NIH policy, so projects should be structured around computational methods, data science, model development, validation using existing datasets or appropriate non-clinical research designs, and other non-clinical-trial activities.
Eligibility is limited to small businesses, consistent with SBIR rules. The FOA also clearly limits foreign participation: non-U.S. entities (foreign institutions) cannot apply, and non-U.S. components of U.S. organizations are not eligible to apply. However, it notes that "foreign components" as defined in the NIH Grants Policy Statement may be allowed, which typically means that a U.S.-based applicant might be able to include certain foreign activities or collaborations if they meet NIH requirements and are well-justified, but the applicant organization itself must be eligible and U.S.-based.
Key administrative details provided include the agency (NIH), the original closing date (2021-03-29), and the creation date (2020-12-10). The award ceiling and expected number of awards are not specified in the provided source text, which means applicants would need to consult the full FOA or NIH documentation for budget limits, project period details, and review considerations. Overall, the opportunity is aimed at accelerating innovation from the small business sector in AI/ML methods that can make environmental health science more predictive, more efficient, and more informative for real-world decisions about chemical hazards, exposure impacts, and health risks.Apply for RFA ES 21 002
- 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 (R43 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.
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Frequently Asked Questions (FAQs)
What is the name of this grant opportunity?
The opportunity is titled "Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R43 Clinical Trial Not Allowed)."
Which agency is offering this funding opportunity?
The funding announcement is from the National Institutes of Health (NIH).
What type of funding mechanism is this?
This is a Phase I SBIR (Small Business Innovation Research) opportunity under the R43 mechanism, intended for early-stage feasibility and proof-of-concept work.
What is the Funding Opportunity Number (FOA number)?
The Funding Opportunity Number listed is RFA-ES-21-002.
What CFDA number is associated with this opportunity?
The CFDA number provided is 93.113.
Is this a discretionary grant?
Yes. The announcement is described as a discretionary grant.
What is the main purpose of the program?
The program is intended to help small businesses create new artificial intelligence (AI) and machine learning (ML) methods that strengthen environmental health science, with a focus on improving environmental health decisions and research workflows.
What kinds of projects does the FOA want to support?
The FOA emphasizes AI/ML method development that can improve toxicology and exposure science workflows. It is oriented toward developing promising methodologies that, with later development and validation beyond Phase I, could meaningfully enhance how environmental health science is conducted and applied.
Is the focus on building a product or doing basic research?
Based on the description provided, the focus is on early-stage, high-potential technical development (feasibility and proof-of-concept) rather than basic research alone, and rather than full-scale deployment.
How does the FOA describe the role of AI/ML in environmental health sciences?
AI and ML are framed as tools that can make toxicity prediction more accurate, reduce uncertainty in hazard identification, and improve risk-related decision-making. The FOA also highlights using AI/ML to prioritize chemicals for more relevant, efficient, or targeted testing.
Why is chemical prioritization mentioned as an important need?
The description notes a practical constraint in environmental health: resources often do not allow comprehensive testing of all compounds at the same depth. AI/ML methods that help prioritize chemicals can support more efficient and targeted testing strategies.
Does the FOA address gaps in toxicity assessment data?
Yes. A major goal described is to address gaps in existing toxicity assessment data and knowledge, including incomplete datasets, inconsistent evidence across studies, and limited mechanistic understanding for many chemicals.
What kinds of approaches are encouraged for dealing with data gaps?
The FOA encourages approaches that can identify where gaps exist, suggest what new data would be most valuable, and potentially help fill those gaps through predictive modeling or by integrating diverse sources of information.
Does the opportunity include exposure science and health outcomes?
Yes. The description indicates interest in methods that advance understanding of how human exposures translate into health outcomes, including differences in susceptibility.
What does "differences in susceptibility" mean in the context given?
As described, it refers to the idea that exposures do not affect all people equally, and that AI/ML methods could help disentangle how exposure patterns, biological variability, and other contributing factors relate to adverse health effects.
What does Phase I (R43) mean for project scope?
Phase I is intended for feasibility, proof-of-concept work, and early technical validation. Applicants are expected to show that the AI/ML approach is technically plausible and promising enough to justify later-stage development, additional validation, and commercialization or broader use.
Are clinical trials allowed under this funding opportunity?
No. The opportunity is labeled "Clinical Trial Not Allowed," meaning proposed research should not include clinical trial activities as defined by NIH policy.
If clinical trials are not allowed, what types of activities are implied to be appropriate?
The description indicates projects should be structured around computational methods, data science, model development, and validation using existing datasets or other appropriate non-clinical research designs, as well as other activities that do not meet NIH's definition of a clinical trial.
Who is eligible to apply?
Eligibility is limited to small businesses, consistent with SBIR rules.
Can non-U.S. entities apply?
No. The description states that non-U.S. entities (foreign institutions) cannot apply.
Are non-U.S. components of U.S. organizations eligible to apply?
No. The description indicates that non-U.S. components of U.S. organizations are not eligible to apply.
Can a U.S.-based applicant include any foreign work or collaboration?
The description notes that "foreign components" (as defined in the NIH Grants Policy Statement) may be allowed. This typically means a U.S.-based, eligible applicant might include certain foreign activities or collaborations if they meet NIH requirements and are well-justified, but the applicant organization itself must be U.S.-based and eligible.
What is the closing date shown in the provided information?
The original closing date listed is 2021-03-29.
What is the creation date shown in the provided information?
The creation date listed is 2020-12-10.
Is the award ceiling provided in the information shown?
No. The award ceiling is not specified in the provided source text.
Is the expected number of awards provided in the information shown?
No. The expected number of awards is not specified in the provided source text.
What should applicants do to confirm budget limits or project period details?
Because the award ceiling and other details are not included in the provided excerpt, applicants would need to consult the full FOA or NIH documentation to confirm budget limits, project period information, and review considerations.
What is the overall goal of the opportunity as described?
Overall, the opportunity aims to accelerate innovation from the small business sector by supporting AI/ML methods that make environmental health science more predictive, more efficient, and more informative for real-world decisions about chemical hazards, exposure impacts, and health risks.
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| Funding Opportunity |
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| Application of Artificial Intelligence and Machine Learning for Advancing Environmental Health Sciences (R41 Clinical Trial Not Allowed) Apply for RFA ES 21 003 Funding Number: RFA ES 21 003 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Immune Development in Early Life (IDEaL) (U19 Clinical Trial Not Allowed) Apply for RFA AI 20 078 Funding Number: RFA AI 20 078 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $1,000,000 |
| Immune Development in Early Life (IDEaL) (U01 Clinical Trial Not Allowed) Apply for RFA AI 20 077 Funding Number: RFA AI 20 077 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $500,000 |
| Transition to Independent Environmental Health Research (TIEHR) Career Award (K01 Clinical Trial Required) Apply for PAR 21 171 Funding Number: PAR 21 171 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Transition to Independent Environmental Health Research (TIEHR) Career Award (K01 Independent Basic Experimental Studies with Humans Required) Apply for PAR 21 170 Funding Number: PAR 21 170 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Transition to Independent Environmental Health Research (TIEHR) Career Award (K01 Clinical Trial Not Allowed) Apply for PAR 21 172 Funding Number: PAR 21 172 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| SBIR E-Learning for HAZMAT and Emergency Response (R43/R44 Clinical Trial Not Allowed) Apply for RFA ES 21 005 Funding Number: RFA ES 21 005 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| CCRP Initiative: Chemical Threat Agent-induced Pulmonary and Ocular Pathophysiological Mechanisms (R01 Clinical Trial Not Allowed) Apply for RFA ES 21 006 Funding Number: RFA ES 21 006 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Innovative Approaches for Improving Environmental Health Literacy (R41/R42 Clinical Trial Not Allowed) Apply for RFA ES 21 009 Funding Number: RFA ES 21 009 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Innovative Approaches for Improving Environmental Health Literacy (R43/R44 Clinical Trial Not Allowed) Apply for RFA ES 21 008 Funding Number: RFA ES 21 008 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Maintaining and Enriching Environmental Epidemiology Cohorts to Support Scientific and Workforce Diversity (U24 Clinical Trial Not Allowed) Apply for RFA ES 22 001 Funding Number: RFA ES 22 001 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $250,000 |
| Revolutionizing Innovative, Visionary Environmental Health Research (RIVER) (R35 Clinical Trial Optional) Apply for RFA ES 22 002 Funding Number: RFA ES 22 002 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Accelerating the Pace of Child Health Research Using Existing Data from the Adolescent Brain Cognitive Development (ABCD) Study (R21-Clinical Trial Not Allowed) Apply for PAR 22 138 Funding Number: PAR 22 138 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Accelerating the Pace of Child Health Research Using Existing Data from the Adolescent Brain Cognitive Development (ABCD) Study (R01-Clinical Trial Not Allowed) Apply for PAR 22 137 Funding Number: PAR 22 137 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| SBIR E-Learning for HAZMAT and Emergency Response (R43/R44 Clinical Trial Not Allowed) Apply for RFA ES 22 004 Funding Number: RFA ES 22 004 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $200,000 |
| Research to Action: Assessing and Addressing Community Exposures to Environmental Contaminants (R01 Clinical Trial Optional) Apply for PAR 22 210 Funding Number: PAR 22 210 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
| Centers for Oceans and Human Health 4: Impacts of Climate Change on Oceans and Great Lakes (COHH4) (P01 Clinal Trial Optional) Apply for RFA ES 22 005 Funding Number: RFA ES 22 005 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $950,000 |
| Biological Basis for how Environmental Exposures Impact Risk for Psychiatric Disorders (R01 Clinical Trial Not Allowed) Apply for RFA ES 22 008 Funding Number: RFA ES 22 008 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $400,000 |
| Biological Basis for how Environmental Exposures Impact Risk for Psychiatric Disorders (R21 Clinical Trial Not Allowed) Apply for RFA ES 22 009 Funding Number: RFA ES 22 009 Agency: National Institutes of Health Category: Environment, Health Funding Amount: $275,000 |
| Utilizing Telomere Status to Reveal Molecular Mechanisms Underlying Susceptibility and Resiliency in Response to Environmental Exposures (R01 Clinical Trial Not Allowed) Apply for RFA ES 22 007 Funding Number: RFA ES 22 007 Agency: National Institutes of Health Category: Environment, Health Funding Amount: Case Dependent |
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