Hacking the Immune Response to Infection in Chronic Obstructive Pulmonary Disease

Efforts to treat chronic diseases are often obstructed by the complexity of the biological mechanisms supporting pathology. Here, we apply a novel method for discrete logical modeling of biological systems to the problem of infectious exacerbations in chronic obstructive pulmonary disease (COPD), using data from human clinical studies to constrain the selection of decisional logic parameters. We obtained two candidate models satisfactorily adhering to the available data. A predicted resting state with no activation of inflammatory markers was supported natively in one model, and could be induced following onset of an exacerbation episode in either model by applying targeted suppression of IL-4 concurrently with at least one additional inflammatory marker including IL-1B. Recapitulating a failed clinical trial of anakinra, suppression of IL-1B alone was not predicted to be sufficient. Instead, these results suggest that the efficacy of IL-1B suppression in the treatment of COPD might be improved by the added concurrent suppression of IL-4 and CCL4 or IL-4 and IL-17A.