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NSF · NSFNSF

PFI-RP: Improving Inhaler Design and Efficacy with a Novel AI-assisted Digital Human Testing Platform

Chenang Liu·Oklahoma State University, OK·2023–2026·COMPLETED
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INSTITUTION

Oklahoma State University, OK

PRINCIPAL INVESTIGATOR

Chenang Liu

FUNDING

$550K

YEAR

2023

MOONBASE SCORE

48/100

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Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project is the availability of a virtual human testing platform for inhaler innovation, enhancing drug delivery efficiency for better lung disease treatment. The project is intended to refine and improve an “all-in-one virtual human testing platform” that can quantify airflow and the inhalation of aerosolized medication emitted from various types of inhalers in healthy and diseased lungs, as well as drug translocations in human body via a non-invasive, cost-effective, and efficient manner. The virtual testing platform is expected to save approximately 66% of the cost and 90% of the time for inhaler innovation and approval. Therefore, the project could improve the targeted drug delivery to small airways, potentially reducing inflammation and improving the pulmonary health of patients with chronic lung diseases. The commercialization of this virtual testing platform may also accelerate innovations in inhalation device design engineering for pharmaceutical companies and promote pulmonary healthcare research. The proposed project will develop a virtual human testing platform by: (1) integrating disease-specific representative whole-lung geometries with the assistance of artificial intelligence (AI) and computational lung aerosol dynamics; (2) enabling droplet-vapor interaction simulation for multiple types of inhalers; and (3) designing an easy-to-navigate web-based user interface (UI) for this platform, enabling a widely-accessible way to input desired drug formulation and inhaler design parameters, and downloading bioequivalence data predicted by the virtual human testing platform for evaluating and optimizing inhaler design without the need for expertise in computational fluid-particle dynamics. The project will also advance the scientific knowledge in inhaler innovation by developing an AI-assisted approach to construct representative disease-specific chronic obstructive pulmonary disease (COPD) whole-lung models, which will help pharmaceutical companies understand how inhalers can deliver accurate doses into the disease-specific lung environment. The project will also serve as a non-invasive and cost-effective alternative tool for the comparative bioequivalence study of inhaler engineering and design on population groups with different lung diseases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

computational fluid dynamicsinhaler designAI-assisted platformvirtual human testingdrug delivery efficiencychronic lung diseasesbioequivalence studypulmonary healthaerosol dynamicsnon-invasive testing

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