Chemical engineering professor awarded an FDA grant to improve drug delivery to the lungs
Monday, October 14, 2024
Media Contact: Tanner Holubar | Communications Specialist | 405-744-2065 | tanner.holubar@okstate.edu
Dr. Yu Feng, an associate professor in the School of Chemical Engineering at Oklahoma State University, was recently awarded a $599,999 FDA grant as the principal investigator.
The project titled, “ML-CFD-DEM Based Reduced Order Models (ROM) to Quantify Variability in Inhalers, Drugs, and Users for Evaluating Comparability of Generic OIDP Complex Products,” was inspired by the need to make inhalers more tailored to delivering medicine to specific parts of the lungs in individuals with respiratory issues.
Feng teamed up with co-investigators who are passionate about improving pulmonary health to build a computer-based model that predicts how medicine from dry powder inhalers reaches the lungs.
“I have been given the opportunity to work with experts in different fields, such as Dr. Huimin Wu, pulmonologist; Dr. Changjie Cai, aerosol scientist; and Dr. Chenang Liu, AI and data science specialist,” Feng said.
“I enjoy collaborating because it allows me to learn from areas outside my expertise, and I gain so much valuable knowledge from my colleagues. This team has helped to push boundaries and develop advanced computational tools that improve regulatory science and inhaler innovation that leads to better medical devices for treating lung diseases, ultimately helping people live healthier lives.”
The research aims to create a model that measures how well inhalers work under different conditions. By using computer simulations that demonstrate how the medicine reacts when it's breathed in and considering the inhaler design, the medicine type, the patient's health and the styles in which patients use inhalers, this new digital tool can benefit patients and the pharmaceutical industry.
“One big challenge is figuring out how well an inhaler delivers the medicine, especially since different inhaler designs, medicines, and lung conditions can make it harder to predict,” Feng said.
“To solve this, we used advanced computer modeling techniques that combine different methods, such as computational fluid dynamics, discrete element method, and machine learning, along with detailed aerosol size distribution measurements using state-of-the-art aerosol devices. This allows us to predict how inhalers work with greater accuracy, reducing the need for many physical tests, including animal studies.”
Another exciting aspect of this project is the integration of 3D-printed lungs in an environmentally controlled chamber that mimics the physiological conditions of human lungs. This system allows researchers to test inhalers in a realistic, controlled, and reproducible environment, offering insights into how medicine particles behave in the lungs under various conditions. This technology bridges the gap between traditional computational models and physical testing, providing an innovative approach to improving inhaler performance.
This new digital tool, known as a digital twin and powered by AI, will be able to predict key metrics to assess how well inhalers perform. It will help the FDA evaluate inhalers and assist the pharmaceutical industry in developing new and better inhalers much faster.
The objective is to develop an overall understanding of pulmonary health by creating non-invasive, affordable, and highly accurate computer models.
“Looking ahead, our goal is to refine our digital twin systems further to be compatible with more inhalers, and to be able to further predict the pharmacokinetics in the whole human body,” Feng said.
“In addition to supporting the FDA in regulatory science, we aim to make these tools accessible to clinical doctors, enabling them to tailor treatments for individual patients.”
Story By: Natalie Henderson | natalie.henderson@okstate.edu