Prediction of Efficacious Inhalation Lung Doses via the Use of In Silico Lung Retention Quantitative Structure-Activity Relationship Models and In Vitro Potency Screens

Lung concentrations of a drug are expected to drive the pharmacodynamic response to local inflammation after inhalation delivery, and the only way of determining the efficacious dose has been to measure it directly in animal models. In this study, we present a method to predict efficacious lung doses after inhalation in a rat lipopolysaccharide challenge model from in silico predictions of lung concentration and in vitro measurements only. A quantitative structure-activity relationship (QSAR) model, based on calculated physical properties predicted the partitioning of 34 compounds between lung and plasma. Because it was observed that lung/plasma partitioning correlated with lung concentration, it was possible to use this relationship to predict lung concentration at a given dose and time point. Based on the pharmacokinetic-pharmacodynamic (PKPD) relationship observed, a minimal free lung concentration relative to potency to drive significant inhibition of neutrophilia was established. By using predicted lung concentrations, measured fraction unbound in plasma, and cellular potency, it was possible to estimate an inhaled lung dose that would be expected to achieve this target exposure. These predictions were made for 23 compounds, which were not part of the original QSAR training set, and all except one were predicted to within 3-fold of their measured values. This novel approach shows that by understanding PKPD relationships and drivers for lung affinity after inhalation dosing, it is possible to estimate in vivo lung doses required for efficacy. This methodology provides a useful screening tool to rank candidate compounds and minimizes the use of extensive animal testing.