Combining experimental and computational techniques to understand and improve dry powder inhalers

ABSTRACT

Introduction

Dry Powder Inhalers (DPIs) continue to be developed to deliver an expanding range of drugs to treat an ever-increasing range of medical conditions; with each drug and device combination needing a specifically designed inhaler. Fast regulatory approval is essential to be first to market, ensuring commercial profitability.

Areas covered

In vitro deposition, particle image velocimetry, and computational modeling using the physiological geometry and representative anatomy can be combined to give complementary information to determine the suitability of a proposed inhaler design and to optimize its formulation performance. In combination, they allow the entire range of questions to be addressed cost-effectively and rapidly.

Expert opinion

Experimental techniques and computational methods are improving rapidly, but each needs a skilled user to maximize results obtained from these techniques. Multidisciplinary teams are therefore key to making optimal use of these methods and such qualified teams can provide enormous benefits to pharmaceutical companies to improve device efficacy and thus time to market. There is already a move to integrate the benefits of Industry 4.0 into inhaler design and usage, a trend that will accelerate.

KEYWORDS:

• Computational fluid dynamics (CFD)
• dry powder inhaler (DPI) design
• high-speed imaging
• in vitro deposition
• particle dynamics
• particle image velocimetry (PIV)

Article highlights

• Dry Powder Inhalers (DPIs) are undergoing significant development worldwide.

• Combining in vitro deposition, flow and particle imaging, and computational studies is an optimal research strategy to comprehensively study DPIs.

• Examination of a typical DPI model through the application of these techniques that provide complementary information.

• This integrated approach requires a multi-disciplinary team made up of researchers who are experts in these techniques.

• Internet of Things (IoT) and use of Reduced Order Models (ROMs) will revolutionize the future of inhalers.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The research was supported by the Australian Research Council. The research also benefited from high-performance computing (HPC) resources provided through the National Computational Merit Allocation Scheme (NCMAS). These resources were provided via the facilities of the National Computational Infrastructure (NCI), the Pawsey Supercomputing Centre, and the Multi-modal Australian ScienceS Imaging and Visualisation Environment (MASSIVE) at Monash University. Both NCMAS and the participating facilities are funded by the Australian Government.