From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD

Part of the Methods in Molecular Biology book series (MIMB, volume 1386)

Abstract

Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.

Key words

Clinical decision support Integrated care Comorbidity Disease modeling Knowledge management 

Notes

Acknowledgments

This work was supported by the Swedish Research Council, Stockholm County Council, Torsten Söderberg Foundation, and Karolinska Institutet. The authors thank PITES PI12/01241, Synergy-COPD (FP7, Id:270086), and Comissionat per a Universitats i Recerca de la Generalitat de Catalunya (2009SGR1308, 2009SGR911, and 2009-SGR-393).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.IDIBAPS, Hospital Clínic, CIBERESUniversitat de BarcelonaBarcelonaSpain
  2. 2.Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES)BunyolaBalearic Islands
  3. 3.Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska InstitutetKarolinska University HospitalStockholmSweden
  4. 4.L8:05 Karolinska University HospitalStockholmSweden

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