Evaluation of in vitro in vivo correlations for dry powder inhaler delivery using artificial neural networks.

The aim of these experiments was to investigate the use of artificial neural networks (ANNs) for generating models able to predict the relative lung bioavailability and clinical effect of salbutamol when delivered to healthy volunteers and asthmatic patients from dry powder inhalers (DPIs). ANN software was used to model in vitro, demographic and in vivo data from human subjects for four different DPI formulations containing salbutamol sulfate. In 12 volunteers, a model linking the in vitro aerodynamic characteristics of the emitted dose and volunteer body surface area with the urinary excretion of drug and its metabolite in the 24h period after inhalation was established.

Determination of the relative bioavailability of salbutamol to the lungs following inhalation from dry powder inhaler formulations containing drug substance manufactured by supercritical fluids and micronization.

Purpose: The relative lung bioavailability of salbutamol sulfate particles produced using supercritical fluids (SEDS) and delivered by dry powder inhaler (DPI) was compared with the performance of a conventional micronized drug DPI using the same device design (Clickhaler, Innovata Biomed).

Materials And Methods: Twelve healthy volunteers and 11 mild asthmatic patients completed separate four-way randomised cross-over studies, assessing the relative bioavailability of salbutamol sulfate (urinary excretion method), formulated as SEDS particles (three batches) and micronized particles (Asmasal inhaler, UCB Pharma Ltd). Post-treatment improvements in patient lung function were assessed by measuring FEV(1).