Assessing the combinatorial influence of climate, formulation and device on powder aerosolization using the Taguchi experimental design

In dry powder inhaler therapy, it is often highly desirable to maximise the fine particle fraction (FPF) while reducing the throat and inhaler retention. While there are a number of factors affecting aerosolization, there is at present no combinatorial study comparing the relative importance of each variable on dispersion and retention. In this work, the Taguchi experimental design, suitable for analysing a large number of factors and interactions within a reasonable number of runs, was applied to study the combinatorial effects of climate, air flow, carrier type and inhaler type on the aerosolization of micronized salbutamol sulphate as a model powder. Taguchi analysis revealed that FPF and throat deposition were highly dependent on the airflow rate and inhaler type, while device and capsule retention could be minimised via judicious selection of carrier and inhaler type respectively. The impact of dispersion climate (temperature and humidity) on aerosol penetration and retention was found to be of secondary importance. Analysis via the Taguchi experimental design thus represents a novel and useful approach for dissecting and understanding the large number of confounding variables affecting aerosolization.

There are a large number of confounding variables affecting aerosolization. A combinatorial study comparing the relative importance of each variable on dispersion and retention was pioneered. Analysis via the Taguchi experimental design thus represents a novel and useful approach for dissecting and understanding the large number of confounding variables affecting aerosolization.Figure optionsDownload as PowerPoint slideHighlights
► There are a large number of confounding variables affecting aerosolization.
► A combinatorial study comparing their relative importance was pioneered.
► Analysis via the Taguchi experimental design represents a novel and useful approach.
► Suited for dissecting and understanding the large number of confounding variables