Prediction of site-specific drug deposition via dry powder inhaler using non-invasive real-time particle emission signal monitoring system

TL;DR

This paper introduces a non-invasive method to predict where inhaled drugs deposit in the lungs using real-time particle emission signals from dry powder inhalers.

Contribution

A novel non-invasive, real-time system using photo reflection to predict site-specific drug deposition from dry powder inhalers.

Findings

Particle emission signals strongly correlate with drug deposition in the lungs using a Hill model.

Oropharyngeal drug deposition correlates with flow rate at emission peak using a linear model.

The method enables personalized inhalation performance assessment beyond conventional flow metrics.

Abstract

Accurate evaluation of regional drug deposition within the respiratory tract is essential for optimizing inhalation therapy efficacy and minimizing adverse effects. However, non-invasive, real-time quantitative methods for site-specific drug delivery assessment remain limited.

To develop mathematical models to predict site-specific drug deposition from a dry powder inhaler (DPI) using a non-invasive, real-time photo reflection method (PRM).

Using Symbicort® Turbuhaler® as a model DPI, four inhalation patterns varying in peak flow rate (PFR: 30–60 L/min) and flow increase rate (FIR: 3.2–9.6 L/s2) were simulated using a human inhalation flow simulator. Aerodynamic particle deposition of budesonide was quantified as the fine particle fraction for the whole lung (FPFWL), peripheral airways (FPFPA), and oropharyngeal region using an Andersen Cascade Impactor. Particle emission signals were monitored via PRM. The relationship between particle emission signals and deposition performance was analyzed using four univariate models: linear, logarithmic, Hill, and Emax.

Increased PFR and FIR enhanced drug deposition in both the lungs and oropharyngeal region. FPFWL and FPFPA were strongly correlated with total particle emission intensity over time with the Hill model (R
2 = 0.86 and 0.74 for FPFWL and FPFPA, respectively), reflecting nonlinear deagglomeration. Oropharyngeal deposition correlated with flow rate at particle emission peak, fitting a linear model (R
2 = 0.82), consistent with inertial impaction mechanisms.

Using an in-vitro model, particle emission signals enable the prediction of site-specific drug deposition from DPI, providing non-invasive, real-time indices and offering personalized inhalation performance assessment beyond conventional flow rate metrics.

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