Abstract

One of the critical components of the respiratory drug delivery is the manner in which the inhaled aerosol is deposited in respiratory tract compartments. Depending on formulation properties, device characteristics and breathing pattern, only a certain fraction of the dose will reach the target site in the lungs, while the rest of the drug will deposit in the inhalation device or in the mouth–throat region. The aim of this study was to link the Computational fluid dynamics (CFD) with physiologically-based pharmacokinetic (PBPK) modelling in order to predict aerolisolization of different dry powder formulations, and estimate concomitant in vivo deposition and absorption of amiloride hydrochloride. Drug physicochemical properties were experimentally determined and used as inputs for the CFD simulations of particle flow in the generated 3D geometric model of Aerolizer® dry powder inhaler (DPI). CFD simulations were used to simulate air flow through Aerolizer® inhaler and Discrete Phase Method (DPM) was used to simulate aerosol particles deposition within the fluid domain. The simulated values for the percent emitted dose were comparable to the values obtained using Andersen cascade impactor (ACI). However, CFD predictions indicated that aerosolized DPI have smaller particle size and narrower size distribution than assumed based on ACI measurements. Comparison with the literature in vivo data revealed that the constructed drug-specific PBPK model was able to capture amiloride absorption pattern following oral and inhalation administration. The PBPK simulation results, based on the CFD generated particle distribution data as input, illustrated the influence of formulation properties on the expected drug plasma concentration profiles. The model also predicted the influence of potential changes in physiological parameters on the extent of inhaled amiloride absorption. Overall, this study demonstrated the potential of the combined CFD-PBPK approach to model inhaled drug bioperformance, and suggested that CFD generated results might serve as input for the prediction of drug deposition pattern in vivo.