Creating better monoclonal antibody formulations using AI and automation
Official title: Data-Driven Antibody Formulation in a Self-Driving Lab
Funded at Rutgers, the State Univ of N.j. · NIH-11105060 · Data updated 2026-11-12
This study is working on using smart technology to make better and faster versions of medicines called monoclonal antibodies, which help treat various conditions, by finding the best ingredients to keep them stable and effective, especially for easy injections under the skin.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rutgers, the State Univ of N.j. NIH-funded |
| Lab location | 1 site (Piscataway, United States) |
| Project start | May 1, 2025 |
| Project end | April 30, 2029 |
| Funding year | 2025 |
| Award (this year) | $342,776 |
| NIH project number | 1R01EB037022-01 |
| Project ID | NIH-11105060 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients requiring monoclonal antibody therapies, particularly those needing subcutaneous administration.
✕Less likely to help
Patients who do not require monoclonal antibody treatments or those with conditions not addressed by these therapies may not benefit from this research.
What this research studies
This research focuses on developing advanced methods to formulate monoclonal antibodies (mAbs) more efficiently using artificial intelligence and automation. By leveraging data science techniques, the project aims to identify optimal combinations of safe additives that enhance the stability and performance of mAbs, particularly for subcutaneous dosing. The approach includes prototyping experimental methodologies, conducting AI-driven formulation searches, and integrating these into a self-driving laboratory system. This innovative pipeline could significantly improve the formulation process, making it faster and more effective for future treatments.
Why it matters
Potential benefit: If successful, this research could lead to more effective and stable monoclonal antibody treatments for patients.
How similar studies have gone: Other research has shown promise in using AI and automation for drug formulation, indicating a potential for success in this novel approach.
Where this research is happening
-
Rutgers, the State Univ of N.j.
Piscataway, United States Active
Researchers
- Principal investigator: Gormley, Adam Joseph — Rutgers, the State Univ of N.j.
- Study coordinator: Gormley, Adam Joseph
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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.