Mapping carrier properties for the intended aerodynamic performance space of dry powder inhalers II: a combinational approach using multivariate analysis and in-silico characterization

Joana Pinto


Physiologically based pharmacokinetic (PBPK) models can provide crucial information about the performance space intended for medicinal products. Data generated in-vitro during the development of dry powders inhaler (DPI) products can provide valuable inputs for PBPK modelling. Therefore, the in-vitro performance of salbutamol sulphate (SS) blends with diverse types of lactose particles was investigated using different device types (capsule v/s. reservoir) at distinct airflows and the generated data input into a PBPK model developed for SS. Likewise, the influence of various carrier particle and bulk properties in combination with different device types and airflows could be investigated in-silico. Mechanistic insights into the potential impact of carrier characteristics on the in-vivo performance of a DPI were statistically derived by correlating the distinct carrier characteristics with the performance predicted in-silico. For the capsule-based device it was determined that carriers with a certain fine content (about 50%) resulted in systematically higher doses of SS being available to the lung. The aforementioned predicted performance, was adequate even at very low flow rates, which could reflect a scenario where patients are unable to produce strong inhalation forces. In case of the reservoir inhaler, only the blend with the carrier with the larger mean particle size was able to a deliver notable amounts of SS to the lung. However, the lung dose of SS delivered by the larger carrier was not enough to produce an adequate pharmacokinetic profile of the drug.

Key Message

A PBPK model was developed for SS and the in-vitro performance of blends with a series of lactose grades was investigated using different device types and distinct airflows. Likewise, the influence of the various particle and bulk properties on different in-silico inhalation scenarios could be statistically investigated

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