Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled drugs.

Prediction of local exposure following inhalation of a locally acting pulmonary drug is central to the successful development of novel inhaled medicines, as well as generic equivalents. This work provides a comprehensive review of the state of the art with respect to multiscale computer models designed to provide a mechanistic prediction of local and systemic drug exposure following inhalation. The availability and quality of underpinning in vivo and in vitro data informing the computer based models is also considered. Mechanistic modelling of local exposure has the potential to speed up and improve the chances of successful inhaled API and product development. Although there are examples in the literature where this type of modelling has been used to understand and explain local and systemic exposure, there are two main barriers to more widespread use. There is a lack of generally recognised commercially available computational models that incorporate mechanistic modelling of regional lung particle deposition and drug disposition processes to simulate free tissue drug concentration. There is also a need for physiologically relevant, good quality experimental data to inform such modelling. For example, there are no standardized experimental methods to characterize the dissolution of solid drug in the lungs or measure airway permeability. Hence, the successful application of mechanistic computer models to understand local exposure after inhalation and support product development and regulatory applications hinges on: (i) establishing reliable, bio-relevant means to acquire experimental data, and (ii) developing proven mechanistic computer models that combine: a mechanistic model of aerosol deposition and post-deposition processes in physiologically-based pharmacokinetic models that predict free local tissue concentrations.