Since its inception the Innovative Medicines Initiative (IMI), Europe’s largest public-private initiative aiming to speed up the development of better and safer medicines for patients, has funded, or is in the process of funding, over 30 public-private partnership projects. Many of these projects are centered upon data intensive translational research. Furthermore, there is rapidly expanding interest in the use of integrative analysis approaches, i.e. Systems Biology, that require Knowledge Management (KM) platforms that not only store data but also facilitate the comparative analysis of different data types and the use of advanced analytical and modeling tools. There is also expanding enthusiasm to harness the potential of open data paradigms for better exploitation of independent datasets. There are commercially available KM platforms, but with commercial platforms the legacy of data generated within publically funded multi-partner projects becomes challenging. It is also difficult to allow open access to the data in commercial KM platforms.
Within IMI there is currently no common KM platform and no provision of services that can support data intensive translational research. Such a common platform was envisioned in the original IMI Strategic Research Agenda. Currently, every precompetitive translational study requires bespoke data management and analysis investments. This has resulted in significant and unnecessary individual project overheads and complex IP issues. As a consequence there have been delays in the sharing of translational data and know-how. Most importantly, there is now a substantial risk to the legacy of the transformational datasets being generated within IMI projects. Such a gap means wasteful, redundant translational research investments, and hinders the formation of a cohesive IMI informatics/KM community. The end result is a less than full realization of the potential of IMI projects to diminish bottlenecks in drug and diagnostic development and ultimately reduced pharmaceutical industry productivity.
The main objective of ’Delivering eTRIKS’ is to address this gap by building a sustainable IMI translational research informatics/KM platform – eTRIKS, and to provide sustainable IMI KM services. In order to realize an ‘open’ platform, the development will begin with transMART, an open source KM platform. eTRIKS, however, will not end with transMART. The intent is to build a combined KM/analytics platform that can serve as a base for continued development. A benchmark of success will be the establishment of a sustainable platform and service layer as well as a robust user and developer community.
Support will be provided throughout the life cycle of a translational research project. There will be an initial business and requirements analysis, which will then lead into active data management. Analytical and systems modelling capabilities will also be made available as they are developed and deployed. Hosting support, data curation, user training and help will be supported project by project through ‘account management’ structures that will be overseen by a governance and prioritisation board. In addition the legacy of the project will be preserved through the provision of a long-term data archive on project closure.
In conjunction with project support services and the eTRIKS platform development, an active community will be built to help foster engagement and to incorporate all stakeholders in ongoing and future development. The intent is to develop a business model that enables the eTRIKS platform to persist long after the 5 years of initial IMI funding.
The Delivering eTRIKS consortium was formed around a KM hub and integrative data analysis expertise developed by ICL, CNRS and BioSci Consulting in the course of deploying tranSMART in the IMI project U-BIOPRED, and extend it for supporting collaborative research. This hub of expertise has been augmented by the EFPIA participants of the eTRIKS consortium, as well as UL, IDBS and CDISC who bring expertise in scientific data curation, analysis and modelling, software engineering and biomedical standards respectively.