Medicine is undergoing a revolution that is transforming the nature of healthcare from reactive to preventive and predictive leading to personalized medical care. This transformation process has motivated the concept of the Virtual Physiological Human (VPH) that seeks to develop a scientific methodological and technological framework within which it will be possible to construct models of the human body as a single complex dynamical system. While research in VPH advances, resulting disease model simulators have not reached an acceptable level of maturity to be used in clinical routine. Especially in cancer, this is a highly demanding endeavour. A sound multiscale and multidimensional modelling of the natural phenomenon of cancer is a sine qua non for a clinically exploitable understanding of the disease.

Based on the latest advancements in the field, clinically driven complex multiscale cancer models can produce rather realistic spatio‐temporal simulations of concrete clinical interventions such as radiochemotherapy applied to individual patients. Clinical data processing procedures and computer technologies play an important role in this context. Following clinical adaptation and validation within the framework of clinico‐genomic trials, models are expected to enhance individualized treatment optimization. The latter constitutes the long-term goal of the emergent scientific, technological and medical discipline of in silico oncology. Treatment optimization is to be achieved through experimentation in silico i.e. on the computer. Moreover, provision of insight into tumour dynamics and optimization of clinical trial design and interpretation constitute short‐ and mid‐term goals of this important VPH domain.

With respect to medical knowledge discovery and management, p-medicine will significantly increase their quality in biomedical research. By using data mining patterns to facilitate the standardization of data mining processes, it will become simpler to share results – models, tools, and workflows alike – with the community of researchers. This will give a boost to current approaches to accelerate the collaboration in the data mining of clinical data, as it will become much easier to discover and re-use existing solutions. p-medicine will thus have an impact on the way analysis tools are developed in general and provide the means to bridge the gap between single tools and the way clinically relevant knowledge is extracted using these tools.

Why is p-medicine interesting for scientists? p-medicine aims to…

  • Promote the vision of personalized medicine
  • Facilitate the transfer of findings in basic research to clinical application
  • Combine data securely from heterogeneous sources of individual patients according to legal and ethical requirements
  • Develop the integrated p-medicine Oncosimulator as an in-silico treatment support system
  • Develop clinical decision support tools for the translation of the p-medicine integrative research approach into the practice of healthcare
  • Develop a data warehouse for the storage and retrieval of large, heterogeneous medical data types to be used by the scientific community
  • Develop and implement a reference architecture to flexibly support the whole spectrum of possible biomedical research tools and services
  • Provide data mining tools and solutions for a more effective analysis of biomedical data
  • Establish a service framework for access to biomaterial resources
  • Link the p-medicine environment with important European Research Infrastructure Initiatives

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