Deposition modelling of inhaled aclidinium and budesonide and comparison to gamma scintigraphy measurement
Helena Thörn1, Thomas Lööf1, Gunilla Petersson1, Frans Franek1, Rebecca Fransson2 & Ulrika Tehler2
1AstraZeneca R&D, Pharmaceutical Technology & Development Inhalation, Pepparedsleden 1, Mölndal, 43183, Sweden
2AstraZeneca R&D, Pharmaceutical Sciences, Pepparedsleden 1, Mölndal, 43183, Sweden
A novel physiologically based biopharmaceutical prediction tool, Lung-Sim, is described and used to predict the deposition pattern and the systemic exposure after inhalation. Two previously reported clinical studies for aclidinium bromide (aclidinium) and budesonide were used for this investigation and Lung-Sim predictions were compared to the extracted scintigraphy measurements in man. The predicted total lung deposition for both compounds were in good agreement with previously published gamma scintigraphy data. When investigating the reported and predicted data further, i.e. predicting regional deposition and correlating it to observed regional gamma scintigraphy data, a discrepancy was noticed. The in silico tool predicted significantly higher peripheral/central ratios than observed in the clinical gamma scintigraphy studies. Due to limitations and assumptions with both the prediction tool and the scintigraphy data, it is hard to judge what the most accurate deposition pattern could be. For budesonide the plasma exposure was reported from the gamma scintigraphy study and was seen as a potential validation parameter for the predicted as well as imaging derived deposition pattern. Using Lung-Sim, the plasma exposure of budesonide was slightly overestimated when using the predicted deposition pattern as the input, while the opposite was true when using observed regional deposition data from the gamma scintigraphy study. It is likely that the true regional deposition pattern is somewhere in between what was retrieved using the two different methods. More sophisticated imaging techniques in combination with physiologically based modelling have the potential to exceed the current understanding of regional lung deposition.