Knowledge management for modeling airway diseases and improving patient outcomes

Biomax provides the underlying knowledge management infrastructure for the European Commission’s AirPROM (Airway Disease Predicting Outcomes through Patient Specific Computational Modelling) consortium of 34 partners from universities, research institutes, the biopharmaceutical industry, patient organizations, small companies and existing research projects. The consortium aims to build multi-scale computer and physical models of the airway system to better understand asthma and chronic obstructive pulmonary disease (COPD).

Using a systems biology approach, the project has established a knowledge portal to elucidate the relationships between clinical phenotype, genotype, airway structure and physiology with the ultimate goal of developing a patient-specific, multi-scale predictive model that uses gene-environment interaction findings to predict the development of asthma or COPD for each individual patient.

As one of the leading contributors to healthcare costs, airway diseases are a hot area of medical research. Recent developments in CT scanning and functional imaging have improved the analysis of patient-specific airway physiology and disease progression. Several new pharmacological therapies and novel devices for surgical therapy are in development. While these advances will potentially improve outcomes, they are expensive and may not be right for every patient; the challenge is to find the right therapy for the individual patient.

By providing a critical tool to help healthcare providers make the best, most well-informed decisions for individualized treatment, the AirPROM consortium hopes to bridge the gaps in clinical management and individual treatment of airways disease. With clinicians involved from the beginning of the project, the AirPROM consortium aims to develop patient-specific models that truly impact personalized medicine.

Using the BioXM™ Knowledge Management Environment, Biomax provides the consortium’s comprehensive airway disease knowledge portal that provides a framework for analysis and data mining. The AirPROM knowledge portal provides a secure and sustainable infrastructure that semantically integrates:

  • Existing biomedical knowledge from allied consortia and public databases
  • Validated micro-scale and macro-scale computational models of healthy, diseased and treated lungs
  • Clinical data from different studies and disciplines
  • Imaging and experimental data
  • “omics” data on genetic gene expression, proteomics and metabolomics

Using the BioXM platform, the knowledge base supports the AirPROM data analysis and modeling efforts and facilitates sharing, collaboration and publication across the multiple disciplines within the AirPROM project and beyond. With the BioXM technology, the AirPROM knowledge base can rapidly adapt to future needs and research developments. As a comprehensive database of asthma and COPD information brought together from several EU projects, it leaves an important, sustainable legacy for future respiratory disease research — well beyond the AirPROM project.

The AirPROM consortium brings together 34 partners from existing research projects and scientific organizations from some of the leading clinical centers in Europe, including experts in physiology, radiology, image analysis, bioengineering, data harmonization, security and ethics, computational modeling, systems biology and health communication. See the AirPROM website for a complete list of the project partners.

AirPROM is funded by the European Union under the FP7 framework (Grant #270194 FP7-ICT-2009)

  • Cloud and automated computations in modern personalized medicine – AirPROM project perspective Kierzynka M, Adamski M, Fritz A, Galka D, Jones I, Maier D, et al. (2016) Int J Applied Math Inform 10:52–60
    Abstract | Full text
  • Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM) Burrowes KS, De Backer J, Smallwood R, Sterk PJ, Gut I, Wirix-Speetjens R, Siddiqui S, Owers-Bradley J, Wild J, Maier D and Brightling C (2013) Interface Focus 3:2
    Abstract | Full text