Acoustic Emission combined with Multivariate Data Analysis
as a Tool for Characterisation of Formulations for Inhalation
Lars Karlsson, Alex Wimbush, Mikael Boberg, Kyrre Thalberg and Mats Josefson
Pharmaceutical Technology & Development, AstraZeneca R&D Gothenburg, S-43183 Mölndal, Sweden
In this paper, Acoustic Emission combined with Multivariate Data Analysis (AE-MVDA) technology is assessed with regards to the development of formulations for inhalation. The long-term aims of the AE-MVDA and formulation initiative are: 1) to enable prediction of the combined performance of the device plus the formulation to be assessed, and, 2) to provide useful and interpretable data about interactions between formulation and device. In this feasibility study, commercially available carriers and experimental formulations were filled into dry powder inhalers (DPIs), which were analysed on an automated dosing station and in parallel using AE-MVDA. Raw sound data was Fourier transformed to frequency spectra, which were fed into a multivariate analysis software. Principal Components Analysis (PCA), as well as other algorithms, with unit variance scaled data were used for spectral analysis. It was shown that both the DPI audible events, during the load and trig/dose operations, contained potentially useful information related to the performance of the formulation as well as on device/formulation interactions.