New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...
New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...
New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...
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8.3. FROM BIOMARKERS TO BIOPRINTS 171<br />
smaller biobanks as well as existing epidemiological studies rebranding<br />
<strong>the</strong>mselves as “biobanks” (Gibbons et al., 2007).<br />
� Corporate-held biobanks involve <strong>the</strong> collection <strong>of</strong> tissue samples and clinical<br />
data by pharmaceutical companies and clinical research organizations<br />
from clinical trial subjects. Opposed to <strong>the</strong> population-based<br />
biobanks, <strong>the</strong> corporate biobanks are not as well documented or scrutinized<br />
by ethicists, lawyers or social scientists (Corrigan and Williams-<br />
Jones, 2006). This can be partly explained by <strong>the</strong> fact that details about<br />
<strong>the</strong>se biobanks are commercially sensitive and <strong>the</strong>re<strong>for</strong>e mostly kept secret.<br />
There is evidence that pharmaceutical companies such as Novartis,<br />
Roche and Pfizer have been routinely collecting biological samples from<br />
clinical trials and have created large repositories <strong>of</strong> tissues with assigned<br />
patient in<strong>for</strong>mation (Lewis, 2004).<br />
� Disease specific biobanks which are established by disease advocacy organizations<br />
with <strong>the</strong> aim <strong>of</strong> producing <strong>the</strong>rapies <strong>for</strong> people with rare<br />
genetic conditions. One <strong>of</strong> <strong>the</strong> earliest examples is PXE International<br />
(Terry et al., 2007) collecting tissues and patient in<strong>for</strong>mation <strong>of</strong> people<br />
affected by <strong>the</strong> rare genetic disorder <strong>of</strong> pseudoxanthona elasticum<br />
(Zarbock et al., 2007).<br />
Biobanks as a basis <strong>for</strong> better drug development<br />
Biobanks have been part <strong>of</strong> what has been called <strong>the</strong> biotechnology revolution<br />
(Nightingale and Martin, 2004) - a view that was shared by governments,<br />
academics and industry. In short, it was believed that significant benefit would<br />
come from genomics and biotechnology (by using <strong>the</strong>se biobanks) <strong>for</strong> drug<br />
development, healthcare and <strong>the</strong> economy in general. This was one <strong>of</strong> <strong>the</strong><br />
main reasons <strong>for</strong> governmental support in <strong>the</strong> creation <strong>of</strong> large populationbased<br />
biobanks.<br />
Thus, <strong>the</strong> central expectation <strong>of</strong> biobank research is that it will enhance<br />
diagnosis, prevention and treatment <strong>of</strong> diseases, leading to an improvement in<br />
<strong>the</strong> health <strong>of</strong> <strong>the</strong> general population and in particular subgroups. This hope<br />
is based on <strong>the</strong> main assumption that analysis <strong>of</strong> biobanks will lead to better<br />
understanding <strong>of</strong> diseases which is usually coupled to <strong>the</strong> identification <strong>of</strong><br />
biomarkers <strong>for</strong> a particular disease. There is no doubt that <strong>the</strong> establishment<br />
<strong>of</strong> cooperative human tissue banks or research networks can greatly facilitate<br />
<strong>the</strong> large-scale validation <strong>of</strong> biomarkers.<br />
8.3 From Biomarkers to Bioprints: Enabling In<strong>for</strong>mation-Based<br />
Medicine<br />
The concept <strong>of</strong> personalized medicine embodies <strong>the</strong> belief that a drug is not<br />
simply effective or ineffective. Ra<strong>the</strong>r, it is likely to be more effective in<br />
some people and less effective or even harmful in o<strong>the</strong>rs. Thus, personalized<br />
medicine can improve <strong>the</strong> potential <strong>for</strong> successful, sophisticated evaluation <strong>of</strong><br />
<strong>the</strong> balance <strong>of</strong> risk and benefit. The promise <strong>of</strong> genomics and proteomics is<br />
largely built on <strong>the</strong> <strong>the</strong>ory that <strong>the</strong>se technologies will enable us to judge <strong>the</strong>se<br />
risk and benefit consideration using much smaller, focused groups <strong>of</strong> patients<br />
than be<strong>for</strong>e. As a consequence, <strong>the</strong> concept <strong>of</strong> single-source biomarkers (e.g.<br />
just a genomic SNP or just proteomics peptide modification) used to make