Validation of a General In Vitro Approach for Prediction of Total Lung Deposition in Healthy Adults for Pharmaceutical Inhalation Products

Abstract

Abstract

Background:

A validated method to predict lung deposition for inhaled medication from in vitro data is lacking in spite of many attempts to correlate in vitro and in vivo outcomes. By using an in vivo-like in vitro setup and analyzing inhalers from the same batches, both in vitro and in vivo, we wanted to create a situation where information from the in vitro and in vivo outcomes could be analyzed at the same time.

Method:

Nine inhalation products containing either budesonide or AZD4818 were evaluated. These comprised two pressurized metered dose inhalers (pMDIs), a pMDI plus a spacer, four dry powder inhalers, and two dosimetric nebulizers. In vitro, an in vivo-like setup consisting of anatomically correct inlet throats were linked to a flow system that could replay actual inhalation flow profiles through the throat to a filter or to an impactor. In vivo, total lung deposition was measured in healthy adults by pharmacokinetic methods.

Results and Conclusion

: We could show that the amount of drug escaping filtration in a realistic throat model under realistic delivery conditions predicts the typical total lung deposition in trained healthy adult subjects in the absence of significant exhaled mass. We could further show that by using combinations of throat models and flow profiles that represent realistic deviations from the typical case, variations in ex-cast deposition reflect between-subject variation in lung deposition. Further, we have demonstrated that ex-cast deposition collected either by a simple filter or by a cascade impactor operated at a fixed flow rate using a mixing inlet, to accommodate a variable flow profile through the inhaler, predicts equally well the lung deposited dose. Additionally, the ex-cast particle size distribution measured by this method may be relevant for predicting exhaled fraction and regional lung deposition by computational models.