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 Guest Commentary: Generating more knowledge from data and more value from studies
June 2011
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Until recently, the role of the information scientist has
largely focused on creating better, more efficient tools and ways to search,
access, manage, manipulate and retrieve relevant data. These tools have evolved
over time to become more intuitive and integrated, with expert systems that
enable the end user to conduct more meaningful analysis, visualization and data
mining.
But who is the end user? More often than not, it’s the
biologist, the chemist and other researchers on the project team. In principle,
this makes sense. The problem is that data sources are growing in volume and
complexity, with a diversity of formats that extends beyond the written word to
include pictures, videos and podcasts. The diversity, complexity and volume of
data also go beyond the technical expertise of the project scientist who rarely
has the technical, computational or quantitative skills to extract the most
meaning from the universe of available and relevant data.
The change that’s needed is not just in software or systems,
but in who drives those systems. Information scientists often have both the
scientific training and the necessary computational skills to assist project
teams with complex analysis and interpretation, yet even they often overlook
how critical their function is in the excitement of using a new technology or
developing a better tool. So, culture and convention work against them, and
they are rarely considered for this more involved and expert user role.
Translational research, by its very nature, incorporates
diverse data from multiple areas: disease information, target sequence, lead
compounds, animal models, drug toxicity, medical records and drug metabolism, among
many others. To further complicate the integration and standardization of data
are the various types of data, parameters, formats, sources and standards—or
lack of standards. The more sources there are to consider, the harder it is to
make comparisons and evaluate content across all dimensions.
The tools of informatics have continued to improve, enabling
better access, integration and mining of data. However, even the best tools
fall short if drug project teams lack the involvement of those who know best
how to use them, know how to interpret their content and know enough to
understand the questions that need to be asked and answered.
This speaks to the cultural and skill-based challenges
involved in getting more value from data. The nature of drug discovery and
development remains an information-rich endeavor, and in order to increase
success rates, we must make better use of our data—and our information science
experts.
The role of the information scientist can be likened to an
advisor from a data perspective, much like the role of a financial advisor at a
brokerage firm. Financial advisors listen to your goals, find out what you’re
trying to achieve with your investments, pull together a number of options for
you to consider and then execute your agreed-upon plan. While it’s possible to
plan your financial future without the help of a financial advisor, having one
adds the expert knowledge of someone who knows how to find and assess
appropriate alternatives. Similarly, the information scientist can be a “one-stop
shop” for the project team, looking at the totality of information, helping
with interpretation and suggesting different research questions to pursue or
even identifying questions or sub-questions that existing data can answer.
Information scientists play an evolving role in project
teams. Once purely a data resource, with a narrow focus on technology, now they
add more value by tapping their broad understanding of the science and of business
needs. Instead of asking, “what question do you want me to answer?” the
information scientists needs a different approach: “Tell me the problem you’re
looking to solve, so together we can formulate the most meaningful question and
I can help you use the information available to answer it.”
Two real-life examples come to mind that demonstrate the
value of having this kind of information expert on project teams. In one case,
the expert information scientist was able to circumvent the running of assays
by recognizing that the answer to a question already existed. The team members
had been looking at slices of the picture and didn’t see the totality of inputs
or that the answer to one of their questions was already available in existing
data if analyzed differently, across studies. Having the answer to one question
meant that a six-month study did not need to be run or repeated; instead, a
different one could be run to test the hypothesis and glean new knowledge.
A second example illustrates the value of being connected
within an information network and contributing to project memory. By
understanding the competitive landscape and the value of learning from others’
experience, the information scientist discovered a conference that would be
addressing research relevant to a specific project. The conference would be
held in another part of the world, without any attendance by project team
members, so the information scientist worked to gain access to the proceedings
and important new knowledge.
For information scientists to be fully accepted into project
teams, they need to sharpen their quantitative and analytical skills to handle
the complex analysis and interpretation of data—and to trust, or refresh, their
scientific training. They need to be more actively engaged as core members,
using their scientific and technical knowledge to determine what information
can best answer a particular therapeutic or business question. Their
involvement should lead the team to a greater level of confidence in making
decisions, and the ability to make them sooner, in crucial aspects of drug
projects.
Dr. Anastasia
Christianson is senior director of R&D Information at AstraZeneca in
Wilmington, Del. She is responsible for delivering the information needs of the
Central Nervous System & Pain Innovative Medicines unit and Global Product
teams, Personalized Healthcare and Biomarkers and R&D Strategy, Portfolio
and Performance. Anastasia obtained her
Ph.D. in Biological Chemistry from the University of Pennsylvania in 1989,
followed by postdoctoral training at Harvard University in Cellular and
Developmental Biology.
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