Development of a Novel Quantitative Structure-Activity Relationship Model to Accurately Predict Pulmonary Absorption and Replace Routine Use of the Isolated Perfused Respiring Rat Lung Model.

PURPOSE

We developed and tested a novel Quantitative Structure-Activity Relationship (QSAR) model to better understand the physicochemical drivers of pulmonary absorption, and to facilitate compound design through improved prediction of absorption. The model was tested using a large array of both existing and newly designed compounds.

METHODS

Pulmonary absorption data was generated using the isolated perfused respiring rat lung (IPRLu) model for 82 drug discovery compounds and 17 marketed drugs. This dataset was used to build a novel QSAR model based on calculated physicochemical properties. A further 9 compounds were used to test the model's predictive capability.

RESULTS

The QSAR model performed well on the 9 compounds in the "Test set" with a predicted versus observed correlation of R(2) = 0.85, and >65% of compounds correctly categorised. Calculated descriptors associated with permeability and hydrophobicity positively correlated with pulmonary absorption, whereas those associated with charge, ionisation and size negatively correlated.

CONCLUSIONS

The novel QSAR model described here can replace routine generation of IPRLu model data for ranking and classifying compounds prior to synthesis. It will also provide scientists working in the field of inhaled drug discovery with a deeper understanding of the physicochemical drivers of pulmonary absorption based on a relevant respiratory compound dataset.