Abstract
The biopharmaceutical industry is grappling not only with sheer data volume
but with the ability of researchers to extract information through
identification and contextual analysis of those data that are relevant to a
particular set of investigations.
This report examines:
- Techniques, technology, and software used in life science data mining
- Data mining for early preclinical safety assessments
- Data mining in clinical trials
- Data mining in pharmacovigilance
- Business models and solutions in drug development bioinformatics
- The mountain of data generated and stored is growing ever-higher. The
information content of life science data is multidimensional and not readily
accessible by merely looking at the output. Unless such data can be put into
proper context and interpreted - i.e., mined - their value is only in their
potential. Data Mining in Drug Development and Translational Medicine examines
data mining challenges and approaches in pharmaceutical R&D.
The pharmaceutical industry has made decisive moves to improve the
predictiveness of early-stage drug safety testing. These efforts generate
large amounts of data, in which the clue to safety-related, potential
“red flags” can be buried. In this context we examine options for
mining types of text data, “pathway mining” for pathway-related
effects of a compound, and the multidimensional output of high-content
screening methods. Also examined are approaches to mining data generated in
preclinical trials for identification of toxicity signatures.
Much more clinical trial data are captured than are actually analyzed to build
the regulatory data file. Clinical databases can thus be mined for information
that the respective study was not explicitly designed to provide. Data Mining
in Drug Development and Translational Medicine describes how data mining from
investigational human trials can reveal hidden information that has the
potential to massively improve the understanding of drug mechanisms, the
efficacy and side effect behavior of drug candidates in various patient
subpopulations, and even the integrity of clinical investigators. We look at
text mining of literature and patent databases, which offers the possibility
for knowledge discovery concerning activity in a particular field of
therapeutic development from many different angles.
Pharmacovigilance is a field where large volumes of interconnected data have
to be analyzed in many dimensions. We describe various databases used in
support of post-market drug safety evaluation, including those maintained by
the FDA, WHO, and EMEA. Data mining algorithms applied to pharmacovigilance
databases and efforts to bring separate databases into full compatibility with
one another are described. Case studies illustrating the use of data mining
and analysis to investigate relationships between marketed drugs and adverse
events are presented.
Data Mining in Drug Development and Translational Medicine concludes by
profiling the most significant vendors that either offer dedicated solutions
for data mining in drug development and pharmacovigilance, or provide more
general commercial data mining solutions that have been successfully adapted
and applied to these endeavors.
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