Biomedical Engineering Reference
In-Depth Information
Article-level information. Here the disease and
mechanism terms are highlighted as well as any
synonyms on record
Figure 14.7
called for each of the different interactions that could be performed. The
main visualisation was built using a commercial platform - Adobe Flex.
This technology was already in use in AstraZeneca and had been proven
to handle over 1 million data points yet still be extremely responsive.
In addition, many more dimensions can be incorporated into this view,
including target effi cacy, safety liabilities, patent position, internal
knowledge and tissue expression. SOLR is fl exible enough to handle
many different types of data and could be leveraged to accommodate
this, whereas the Flex visualisation has already been tested and can
handle tens of dimensions with millions of rows of data.
Before this approach, our re-positioning opportunities would be
opportunistic and not systematic. This technology enables an impressive
summary of a huge volume of complex data and the generation of
mechanism-disease landscapes. Without SOLR and Flex, this would be
possible, but SOLR enables a rapid up-to-date system to be repeated
weekly and Flex provides suffi cient speed even when faced with large
data numbers or statistical measures.
￿ ￿ ￿ ￿ ￿
14.7.3 Arise - biological process maps of how
AstraZeneca drugs work
Another approach taken to professionalise our drug repositioning efforts
was to develop internal knowledge of understanding around how the
company's drugs work at the cell and tissue level.
Successful repositioning in the past has been through one of two
routes: (1) serendipity or (2) biological understanding. Using the classic
and well-known repositioning example of sildenafi l, it was originally
 
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