Many inhaled drugs are poorly water soluble, and the dissolution rate is often the rate-limiting step in the overall absorption process. To improve understanding of pulmonary drug dissolution, four poorly soluble inhalation compounds (AZD5423 (a developmental nonsteroidal glucocorticoid), budesonide, fluticasone furoate (FF), and fluticasone propionate (FP)) were administered as suspensions or dry powders to the well-established isolated perfused 4 rat lung (IPL) model. Two particle size distributions (d50 = 1.2 mu m and d50 = 2.8 mu m) were investigated for AZD5423. The pulmonary absorption rates of the drugs from the suspensions and dry powders were compared with historical absorption data for solutions to improve understanding of the effects of dissolution on the overall pulmonary absorption process for poorly soluble inhaled drugs. A physiologically based biopharmaceutical in silico model was used to analyze the experimental IPL data and to estimate a dissolution parameter (K-ex vivo). A similar in silico approach was applied to in vitro dissolution data from the literature to obtain an in vitro dissolution parameter (Kin vitro). When FF, FP, and the larger particles of AZD5423 were administered as suspensions, drug dissolution was the rate-limiting step in the overall absorption process. However, this was not the case for budesonide, which has the highest aqueous solubility (61 mu M), and the smaller particles of AZD5423, probably because of the increased surface area available for dissolution (d50 = 1.2 mu m). The estimated dissolution parameters were ranked in accordance with the solubility of the drugs, and there was good agreement between k(ex vivo) and k(in vitro). The dry powders of all the compounds were absorbed more slowly than the suspensions, indicating that wetting is an important parameter for the dissolution of dry powders. A wetting factor was introduced to the in silico model to explain the difference in absorption profiles between the suspensions and dry powders where AZD5423 had the poorest wettability followed by FP and FF. The IPL model in combination with an in silico model is a useful tool for investigating pulmonary dissolution and improving understanding of dissolution-related parameters for poorly soluble inhaled compounds.

The ex vivo isolated perfused rat lung (IPL) model has been demonstrated to be a useful tool during drug development for studying pulmonary drug absorption. This study aims to investigate the potential use of IPL data to predict rat in vivo lung absorption. Absorption parameters determined from IPL data (ex vivo input parameters) in combination with intravenously determined pharmacokinetic data were used in a biopharmaceutics model to predict experimental rat in vivo plasma concentration-time profiles and lung amount after inhalation of five different inhalation compounds. The performance of simulations using ex vivo input parameters was compared with simulations using in vitro input parameters, to determine whether and to what extent predictability could be improved by using input parameters determined from the more complex ex vivo model. Simulations using ex vivo input parameters were within twofold average difference (AAFE < 2) from experimental in vivo data for all compounds except one. Furthermore, simulations using ex vivo input parameters performed significantly better than simulations using in vitro input parameters in predicting in vivo lung absorption. It could therefore be advantageous to base predictions of drug performance on IPL data rather than on in vitro data during drug development to increase mechanistic understanding of pulmonary drug absorption and to better understand how different substance properties and formulations might affect in vivo behavior of inhalation compounds.

Systemic exposure of inhaled drugs is used to estimate the local lung exposure and assess systemic side effects for drugs with local pharmacological targets. Predicting systemic exposure is therefore central for successful development of drugs intended to be inhaled. Currently, these predictions are based mainly on data from in vitro experiments, but the accuracy of these predictions might be improved if they were based on data with higher physiological relevance. In this study, systemic exposure was simulated by applying biopharmaceutics input parameters from isolated perfused rat lung (IPL) data to a lung model developed in MoBi (R) as an extension to the full physiologically-based pharmacokinetic (PBPK) model in PK-Sim (R). These simulations were performed for a set of APIs with a variety of physicochemical properties and formulation types. Simulations based on rat IPL data were also compared to simulations based on in vitro data. The predictive performances of the simulations were evaluated by comparing simulated plasma concentration-time profiles to clinical observations after pulmonary administration. Simulations using IPL-based input parameters predicted systemic exposure well, with predicted AUCs within two-fold of the observed value for nine out of ten drug compounds/formulations, and predicted Cmax values within two-fold for eight out of ten drug compounds/formulations. Simulations using input parameters based on IPL data performed generally better than simulations based on in vitro input parameters. These results suggest that the developed model in combination with IPL data can be used to predict human lung absorption for compounds with different physicochemical properties and types of inhalation formulations.