Biomedical Engineering Reference
In-Depth Information
16.1
INTRODUCTION
The diffi culty of reconciling animal model data with clinical outcomes has
been leading to a growing consensus that the most valuable data source for
biomedical discoveries is derived from human samples. This recognition is
clearly refl ected in the increasing number of translational medicine and trans-
lational sciences departments across pharmaceutical companies as well as
academic and government-supported initiatives such as Clinical and Trans-
lational Science Awards (CTSA) in the United States (http://www.ctsaweb.org/)
and the Seventh Framework Programme (FP7) of the European Union (EU)
[1], which puts strong emphasis on translating research for human health.
The recent advancement of the idea of precompetitive sharing [2-5] has
been quickly gaining ground. Bioinformaticians from the pharmaceutical
industry are proposing improved collaboration in computational biology and
chemistry between the public domain and the industry [5] by virtualizing
informatics tools, services, and infrastructure. Some notable successes have
already been achieved.
One such example is Merck's partnership with the Moffi t Cancer Center
and Research Institute [6]. They have developed a system which enables
sharing of human subject data in oncology trials. This system is built from
proprietary and commercial components such as Microsoft BizTalk business
process server and Tibco and Biofortis LabMatrix applications but does not
address any data-sharing issues outside of the two institutions.
Some preliminary pilot studies in other pharmaceutical organizations have
been reported [7], but to date no solid evidence for production-level systems
being deployed has been found.
Another example of shared infrastructure is the case of CTSA awardees.
These institutes have recognized the need for more effi cient data sharing (see,
e.g., the proposal for the CTSA Human Studies Database (HSDB) Project [8];
Fig. 16.1). The proposed system concentrates on the study results emanating
from the CTSA awardees and does not capture the wealth of information
generated by institutes which are not part of this grant, and also the proposed
system has not yet been put into wide use according to our best knowledge.
The pharmaceutical companies of Johnson and Johnson have established
translational and biomarker departments and implemented translational
informatics approaches, including building a data warehouse and data-mining
application. The solution is heavily reliant on open-source components, and
thus the implemented resource and the standardized framework it was built
on can form the basis of precompetitive sharing of studies involving samples
from human subjects. This in turn can lead to better understanding of human
biology and pathophysiology, ultimately leading to more effective manage-
ment and treatment of diseases in a collaborative setting. This infrastructure
is a combination of dedicated people, robust processes, and informatics solu-
tion and is called tranSMART [9, 10]. In this chapter the process of building
the system, the technical solution, and application examples are described.
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