Predicting drug efficacy using integrative models for chronic respiratory diseases.

Animal models are vital instruments of the drug discovery process. In addition to assessing the efficacy of candidate molecules, in vivo disease models also help validate the therapeutic potential of molecular targets. Over recent years, several molecules that have shown efficacy in preclinical models of respiratory diseases have failed to translate into new medicines for chronic respiratory conditions such as asthma, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis. As such, many scientists have argued that these systems are of limited value; however, we propose that a more careful and thorough approach to the characterization of these models and the interpretation of data generated using these systems would improve their translational utility. Herein, we describe two key elements of our strategy aiming to improve the predictive nature of these models: 1) Novel bioinformatics methods that can be used to identify animal models that best represent specific patient populations; and 2) Innovative physiological techniques that will improve our ability to discover drugs that can restore the functional capacity of lungs damaged during the course of the disease.