Life Sciences Informatics
in R&D
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The medical world is looking to new technologies to provide
advanced understanding of disease. Effective drugs for
treatment and prevention are needed for many disease areas,
including cardio-vascular disease, cancer, neurological
disorders, infectious diseases, endocrinology, and inflammatory
and chronic degenerative diseases. There is excitement
about the potential biological revolution that will emerge
with understanding the human genome, proteomics and metabolic
pathways. The challenge for pharmaceutical research is
to unravel the pathophysiology of human diseases and thus,
make it possible to identify targets. Now that the first
set of complete human genome data has been reported, the
focus of pharmaceutical and biotech companies needs to
shift from sequencing the genome to understanding the
relationships of genome to diseases and finding new, innovative
drug molecules. Parallel developments of new biological
technologies, such as transcript profiling, allow scientists
to examine almost any biological system in high molecular
resolution. Contemporary drug discovery research is now
focusing on the identification and validation of pharmaceutical
targets in the molecular pathways/systems embedded in
this information. Novel therapeutic interventions are
being developed and evaluated as a result of this research,
and these are expected to be the basis of innovative pharmaceuticals
of the future.
New R&D technologies have dramatically increased
data volume and complexity. The mapping of the human genome,
and the increased use of combinatorial chemistry, high
throughput screening, and other technologies have dramatically
increased the amount of data available to drug discovery
organizations. The amount of public and proprietary data
being generated today is several orders of magnitude larger
than that of the pre-genomics era. Vast amounts of biological
data are being accumulated in the areas of gene sequencing,
gene expression, protein structure, cellular metabolic
processes, cell-signaling pathways, and cell system analysis,
and the volume and complexity of this new data defies
efforts to transform it into information and to integrate
it into existing discovery processes. New technologies
have driven a 10,000-fold increase in the number of compounds
a company can investigate during the discovery phase,
yet the difficulty of managing this unprecedented volume
of data has become one of the biggest bottlenecks in the
drug discovery process.
Life sciences informatics, or the use of computers and
sophisticated algorithms to store, analyze and interpret
large volumes of life sciences data (bio-, cheminformatics),
is essential in order to capture value from this growing
pool of data. By integrating data sources, development
organizations can integrate and analyze genomic information
from multiple sources to discover genes that may represent
the basis for new biological targets, therapeutic proteins,
or diagnostic products. Through curation and annotation
(the checking and re-tagging of data within a database)
even dissimilar information can be integrated, including
gene expression, gene sequence information, SNP data,
functional genomics studies, preclinical pharmacology
and toxicology, and results from clinical development
studies.
In order to benefit fully from the range of new technologies
available to the life sciences industry, discovery organizations
need to develop and implement a cohesive informatics strategy
for managing corporate research and discovery processes.
Management and research personnel need to cooperate to
develop a comprehensive informatics platform that will
meet the organization's present and future data requirements.
Although most development organizations realize the potential
of life sciences informatics to improve research productivity,
few understand how they might integrate informatics into
their current research architecture. Most also have concerns
regarding how the necessary process change, implementation,
and training would be realized.
Few pharmaceutical and biotechnology firms have the experienced
systems IT personnel necessary to architect and implement
an enterprise-wide data infrastructure. However, many
of the large IT systems consultants provide only a standard
solution, or a solution based around their own product,
to the detriment of the client.
Each discovery organization's data management practices
are unique, with its own proven processes and methodologies
for gathering, mining, and storing research data. There
is an opportunity to help management and research personnel
of R&D organizations to implement an informatics strategy
that complements this legacy process and hardware infrastructure.
There is also an opportunity to migrate legacy software
applications into multi-tier client/server applications,
allowing customers to preserve the core functionality
and other benefits of their legacy applications while
eliminating the constraints of legacy system architectures.