Developing vaccines to improve treatment for Type 1 Diabetes
Official title: Machine learning optimized autoimmune therapeutics with a focus on Type 1 Diabetes
Funded at Think Therapeutics, INC. · NIH-10929966 · Data updated 2026-11-12
This study is working on new vaccines to help treat Type 1 Diabetes by finding specific proteins that the immune system mistakenly attacks, and it aims to create a vaccine that can help calm down that immune response, making it better for people with the condition.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Think Therapeutics, INC. NIH-funded |
| Lab location | 1 site (Newton, United States) |
| Project start | September 15, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $306,500 |
| NIH project number | 5R43AI177185-02 |
| Project ID | NIH-10929966 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Type 1 Diabetes who may benefit from new therapeutic approaches.
✕Less likely to help
Patients with other types of diabetes or autoimmune diseases unrelated to Type 1 Diabetes may not receive benefit from this research.
What this research studies
This research focuses on creating new vaccines aimed at treating autoimmune diseases, specifically Type 1 Diabetes (T1D). The team will develop a novel assay called the Multiplexed Multi-antigen Activation Assay (MMAA) to identify self-antigens recognized by T cells in T1D patients. Using these self-antigens, they will design multi-epitope tolerization vaccines and test their effectiveness in expanding regulatory T cells from blood samples of T1D donors. Advanced machine learning techniques will be employed to enhance the vaccine's effectiveness across diverse populations.
Why it matters
Potential benefit: If successful, this research could lead to more effective vaccines that improve treatment outcomes for patients with Type 1 Diabetes.
How similar studies have gone: Other research has shown promise in using machine learning and immunogenicity assays for developing autoimmune therapies, indicating a potential for success in this novel approach.
Where this research is happening
-
Think Therapeutics, INC.
Newton, United States Active
Researchers
- Principal investigator: Gifford, David K — Think Therapeutics, INC.
- Study coordinator: Gifford, David K
Related research
Other NIH-funded projects on the same topics.
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Technological and Educational Approaches to Restore Hypoglycemia Awareness in Type 1 Diabetes
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Treating impaired hypoglycemia awareness in type 1 diabetes with targeted education and technology
This study tests two treatment approaches to help adults with type 1 diabetes who often don’t realize their blood sugar is getting too low, using a mix of technology and education in two large groups.
-
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A UK study tests two different approaches to help adults with type 1 diabetes recognize and respond to low blood sugar by comparing a high-tech insulin system with targeted education.
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How the pancreas environment affects insulin-producing cells in type 1 diabetes
Researchers are studying how the surrounding tissue of the pancreas and its proteins influence insulin-producing cells in people with type 1 diabetes.
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Understanding how hypoglycemia awareness changes in adults with type 1 diabetes and how automated insulin delivery and exercise might help restore it
This study will look at adults with type 1 diabetes to see how their awareness of low blood sugar evolves over time and whether automated insulin delivery and high-intensity exercise can help restore that awareness.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.