Evaluation of carrier size and surface morphology in carrier-based dry powder inhalation by surrogate modeling

Abstract

In this work, design parameters of carrier-based dry powder inhalation were studied using surrogate modeling technique. The surrogate models constructed were then used to evaluate the key design parameters independently, which were otherwise difficult to determine based on experimental studies alone. Artificial neural network (ANN) was chosen as the surrogate modeling technique and models were constructed based on experimental data obtained from the literature. Twenty-eight variables describing the carrier size distribution, density, surface characteristics and operating conditions of dry powder inhaler were used as the input variables and emitted dose (ED) and fine particle fraction (FPF) were used as the output variables. Carrier surface characteristics were evaluated by applying image analysis on carrier SEM images. Genetic algorithm (GA) was used for the selection of important variables to be included in the surrogate models. Sensitivity analysis was also performed to determine the key variables affecting ED and FPF. Key design criteria for carrier-based dry powder inhalation were proposed based on the surrogate models constructed.