Improving the discovery of antibody treatments using machine learning
Official title: REPTOR: accelerating antibody discovery and improving hits with machine learning
Funded at Abterra Biosciences, INC. · NIH-10822485 · Data updated 2026-11-12
This study is working on a new way to find better antibody treatments for diseases like autoimmune conditions and COVID-19, using smart software to speed up the process, so patients can get effective treatments more quickly.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Abterra Biosciences, INC. NIH-funded |
| Lab location | 1 site (San Diego, United States) |
| Project start | April 1, 2020 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $856,396 |
| NIH project number | 2R44GM137688-02 |
| Project ID | NIH-10822485 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with autoimmune diseases or those affected by COVID-19 who may benefit from new antibody treatments.
✕Less likely to help
Patients who do not have autoimmune diseases or COVID-19 may not receive direct benefits from this research.
What this research studies
This research focuses on enhancing the process of discovering antibody therapeutics, which are crucial for treating various diseases, including autoimmune conditions and COVID-19. By developing advanced software that integrates high-throughput sequencing with existing antibody discovery methods, the project aims to streamline and improve the success rate of identifying effective antibodies. The approach leverages the natural immune responses of individuals to better inform the selection of therapeutic candidates. Patients may benefit from faster and more effective antibody treatments as a result of this innovative methodology.
Why it matters
Potential benefit: If successful, this research could lead to quicker and more effective antibody therapies for patients with various diseases.
How similar studies have gone: Other research has shown promise in using high-throughput sequencing for antibody discovery, indicating that this approach could be a significant advancement in the field.
Where this research is happening
-
Abterra Biosciences, INC.
San Diego, United States Active
Researchers
- Principal investigator: Castellana, Natalie — Abterra Biosciences, INC.
- Study coordinator: Castellana, Natalie
Related research
Other NIH-funded projects on the same topics.
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Improving antibody discovery using machine learning techniques
This study is working on using smart computer programs to help find better antibody treatments for COVID-19 faster, so patients can get access to new and effective options more quickly.
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A new antibody profiling platform to help diagnose autoimmune diseases like lupus
A new method will analyze many antibody sequences in blood to aid diagnosis of autoimmune diseases such as lupus.
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REPTOR: Using machine learning to find new antibody treatments
This project is creating new software to help scientists discover better antibody treatments for autoimmune diseases more quickly.
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.