Chronic obstructive pulmonary disease (COPD) claimed 3.2 million lives in 2015, making it the third cause of death worldwide. It is predicted to increase in coming years due to aging populations and thus constitutes an enormous socio-economic burden. Existing assessment strategies neglect the complex, multi-component, and heterogeneous pathophysiology, as well as manifold comorbidities (cardiovascular, metabolic etc.). Therefore, improved COPD diagnosis and classification constitutes an urgent medical need for improved and personalized prevention measures and treatments strategies. The main aim of our project is to develop a tool that will enable effective preventive measures and personalize treatment strategies for COPD by means of systems medicine. This transnational and interdisciplinary project combines clinical scientists, experimentalists, computational and systems biology researchers, as well as a medium sized company. We will develop a systems medicine model of COPD constructed on (i) machine learning clustering of two comprehensive patient cohorts (COSYCONET, CIRO) providing long-term clinical observations, systematic outcome evaluation, biomaterial collections, multiple laboratory measurements, and extensive imaging data of more than 6,000 patients, complemented by (ii) an iterative systems biology framework of modeling and experimental analysis. Based on this multi-scale systems medicine model, we will generate a novel Clinical Decision Support (CDS) software that we will evaluate for patient care in the existing IT infrastructure of hospitals and private practices. As a prototypic demonstrator of applied systems medicine modeling, our tool will enable i) individual and comprehensive treatment and prevention measures for COPD patients ii) significant reductions of socio-economic costs due to less mortality and disability iii) novel insights in the dysregulation of metabolism, immunology and aging in COPD from the underlying model.
Project leader: Nadav Skjøndal-Bar
Institution: Institutt for kjemisk prosessteknologi