Developing new drug combinations to treat multidrug-resistant tuberculosis
A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis
This study is looking for better ways to combine medications to treat tough cases of tuberculosis (TB) using smart computer technology, and it’s designed to help researchers find the best treatment options for patients who need them.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project ID | NIH-10891634 on NIH RePORTER |
What this research studies
This research focuses on creating a new method to identify effective drug combinations for treating multidrug-resistant tuberculosis (TB). Using a machine learning algorithm called INDIGO-MTB, the team will predict how different TB drugs work together, especially under conditions that mimic actual infections. The research will involve testing these combinations in laboratory settings and in mouse models to find the most effective regimens. Ultimately, this work aims to streamline the development of new treatments for TB and potentially other diseases requiring combination therapies.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals diagnosed with multidrug-resistant tuberculosis.
Not a fit: Patients with drug-sensitive tuberculosis or those not infected with tuberculosis may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more effective treatments for patients suffering from multidrug-resistant tuberculosis.
How similar studies have performed: Previous research has shown promise in using machine learning to optimize drug combinations, indicating potential success for this novel approach.
Where this research is happening
Seattle, United States
- University of Washington — Seattle, United States (Active)
Researchers
- Principal investigator: Sherman, David R — University of Washington
- Study coordinator: Sherman, David R
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 below.