Improving the effectiveness of inhalers for patients using machine learning.
ML-CFD-DEM Based Reduced Order Models (ROM) to Quantify Variability in Inhalers, Drugs, and Users for Evaluating Comparability of Generic OIDP Complex Products
This study is looking to improve how well dry powder inhalers work by using smart technology to predict how well the medicine gets to your lungs, taking into account things like the inhaler's design and how you use it, so that everyone can get the best results from their inhalers, whether they are brand-name or generic.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Oklahoma State University Stillwater NIH-funded |
| Lab location | 1 site (Stillwater, United States) |
| Project ID | NIH-11064450 on NIH RePORTER |
What this research studies
This research focuses on enhancing the performance of dry powder inhalers (DPIs) by developing advanced machine learning models that can predict how well inhaled medications reach the lungs. It takes into account various factors such as inhaler design, drug formulation, and individual patient characteristics, including specific lung conditions and how well patients use the devices. By creating more efficient models, the research aims to better understand the variability in inhaler performance and ensure that generic inhalers are as effective as their brand-name counterparts.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients who use dry powder inhalers for respiratory conditions.
Not a fit: Patients who do not use inhalers or have conditions that do not require inhaled medications may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more effective inhalers that improve medication delivery for patients with respiratory conditions.
How similar studies have performed: Other research has shown promise in using machine learning to improve medical devices, indicating potential success for this approach.
Where this research is happening
Stillwater, United States
- Oklahoma State University Stillwater — Stillwater, United States (Active)
Researchers
- Principal investigator: Feng, Yu — Oklahoma State University Stillwater
- Study coordinator: Feng, Yu
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.