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Across every industry managers and workers are finding themselves
continually wading through what is rapidly becoming a mountain of
information. The internet, regulation and computerisation have
all played a part. The laboratory has felt the growth in data particularly acutely. As the pharmaceutical and biotechnology industries strive to bring a greater number of drugs to market more quickly, labs are under pressure to screen a much wider range of prospective drug candidates. Whilst Information Systems (IS) make it easier to manage the volume of data spawned as a result of regulatory and competitive pressures, it also makes copious amounts of data available in volumes that many labs find difficult to manage. The origins of the data mountain: Competition and the need for speed
Companies are endeavouring to reduce time to market for new products
and develop a greater number of new drugs per year. Typically,
the business needs innovative new products to win market share, and
it needs them more quickly than the competition. Consequently,
the lab finds itself under pressure to develop procedures that
facilitate screening an increasing number of prospective drug
candidates at the beginning of a new product development cycle.
Because of this competitive pressure, the number of new chemical
entities entering the latter stages of development each year is
significantly increasing. Each new development attracts a huge
amount of data, particularly as techniques that acquire 3D data or
ascertain genetic data push volumes up even further.
An extra dimension to new product development is brought by the need
to extend patents through the use of new delivery mechanisms.
With many of the major blockbuster brands approaching the end of
their normal patent life, manufacturers are looking for new ways to
deliver the same products. This activity puts further stress
upon laboratory systems and the development process as a whole.
Data storage requirements change significantly as drugs move from R
to D:
Looking specifically at the development part of R&D,
organisations need to make complicated decisions about what to do
with data that describes entities that don’t make it to the next
stage. Increasingly, they may want to look at alternatives to
successful products to see whether they would succeed using a new
variant or a new delivery mechanism, for example.
This goes against the traditional approach of identifying a
successful candidate and taking it forward, then discarding the
rest. It means that many organisations won’t have
decision-making processes in place to pin down which candidates (and
associated data) should be stored, how long for, and who should be
involved in making those decisions. The cost of data
collection, storage and archiving always needs to be balanced
against the potential value of that data to the company. This mountain of data can be highly unstructured, and include findings from presentations, from virtual teams working around the world, from different computer applications, and from field tests run by third party researchers. Paperwork relating to a new drug is literally delivered to the FDA in truckloads. The origins of the data mountain: Regulation
In addition to competition, regulatory pressures also have a
significant effect that further fuels concern regarding data
explosion. The move from paper to electronic record keeping causes
issues in itself, as it implies the need not just for better ways of
proving authenticity within data, but also introduces a series of
decisions that need to be made about how data will be stored and
read in the future. Will the software applications used to
read and store data today be the same ones in use in five years time
when a product is released onto the market, for example? One
reason for the introduction of 21 CFR Part 11 by the FDA was to find
ways to document and prove audit trails on electronic data.
While a paper document can be analysed to see when details have been
changed, it is far more difficult to look at original raw data and
prove exactly when it was captured, stored and altered.
IT and IS directors and lab managers are all developing different
approaches to this challenge, and these may involve electronic
signatures, audit trails and hybrid approaches to paper/electronic
documents. Whichever approach wins out, it will almost
certainly have an effect on the way organisations manage their
growing data volumes. The origins of the data mountain: IS
Technology itself is also playing a part in the size and scope of
the information mountain. The implementation of technology
such as automated testing systems has made laboratories
significantly more productive over the past five years. But
ironically these improvements in productivity are now contributing
to the data mountain, which will only multiply in complexity and
volume over the next few years. Climbing the mountain
So, bearing all of those factors in mind, where should organisations
start when developing a strategy to conquer the mountain of data
they produce and must store? Scimcon’s advice is that it can
only begin with the short, medium and long-term business plan for
the lab, and for the organisation as a whole.
Step one: Match IS, lab and business needs
There is no point in developing an approach that enables labs to
provide data, information and knowledge, only to find that it is not
in line with the business plan and therefore is not required.
If the business aim is to develop a specific number of new products
and derivatives in a set period, then a data strategy should deliver
this aim. It is not just about making lives easier for people
who work in the lab, though this is clearly important. Step two: Assess the role of IT
The next stage is to analyse what technology is in place already.
What systems are in place, what is working well, what is not working
well, and where the gaps exist? The investments required in
information systems to plug those gaps need to be prioritised in
line with business aims and objectives: what investment will enable
the business to move forward most quickly?
The costs involved to implement a new data system for a laboratory
can run into hundreds of thousands of dollars. It is important
to ensure that this investment is channelled into the most
appropriate direction for the business, not just for the lab. Step three: Execution of new information strategies
Implementation of those new systems comes next. Without
introducing bureaucracy, a steering group of high level executives
from the business and the lab environment is needed to make
decisions about when different projects within the overall
Information System (IS) strategy should take place.
Seniority is required at this stage, not just so that funding can be
secured, but also so that appropriate resources can be allocated to
each project. These project teams need to represent all of the
stakeholders involved ensuring buy-in and understanding. The
steering group must also monitor progress of each project against
original targets and business objectives.
Navigating these requirements and ensuring that all stakeholders’
interests are represented is not always easy. There are some
historical and cultural barriers that need to be addressed,
particularly when it comes to making decisions about keeping data on
unsuccessful candidates, for example.
For this reason, it makes sense to work with external experts who
have experience and know-how in building IS strategies that deliver
business objectives. A partner such as Scimcon has first hand
experience with many different organisations, and can help companies
recognise the need for an inclusive approach. It can take a
fresh view of a company and its existing systems, and help set
priorities for investment that deliver value to the business as well
as lab managers, directors and their staff. Tear up your spreadsheets!
Bayer Pharmaceutical’s biotechnology division is a good example of
a company conquering the data challenge. The biotechnology site
analyses thousands of protein samples each year in its quest to
develop medication to battle life-threatening illnesses. To
harness the wealth of information now available for drug discovery,
Bayer Biotech has embarked upon a five-year plan to redesign its
information management to support the research and development
processes more effectively.
Strategic partner Scimcon has been involved in a comprehensive IS
strategy review, including the installation of a new candidate
tracking system. This has enabled Bayer to streamline and
simplify its data management processes, making these far easier for
users to handle and understand.
Ken Kupfer, Head of Biotechnology Scientific Informatics at Bayer
Corporation, says:
“Two years ago, our concept of information management was
bioinformatics. But now, thanks to Scimcon, we see the value of an
integrated approach to information management that supports our
entire R&D process. There has been an immediate business
improvement in that vital information supporting the drug discovery
process is now stored in one central, automated system, which has
replaced the mishmash of Word and Excel documents which we’ve
since been able to rip up and discard.” Staying on top
The information mountain is only going to grow in size over the next
few years. The cost of technology will continue to drop, which will
enable more processes to be automated and more data to be generated
at an ever-increasing level of granularity. Competitive
pressures spurning data will increase, and regulatory requirements
are unlikely to subside.
So the data explosion of the past few years will continue to grow
incrementally. This means that organisations in the
pharmaceutical and biotechnology sectors will need to include data
management into their ongoing strategy. Companies will be
required to invest more resources into lab information systems to
enable them to store more complex and voluminous data.
IS strategies will continue to absorb an ever increasing share of
corporate budgets. As this happens it is critical that
expenditure directly addresses the real needs of the business.
This cannot be simply a question of throwing money at a problem: a
pragmatic approach based on a real understanding of the business,
its goals and its priorities going forward must be adopted. If
it is not, companies run the risk of being crushed by the data
mountain, and failing where more nimble, efficient, data competent
competitors succeed. About Scimcon
Established in 1987, Scimcon has worked with global pharmaceutical,
biotechnology, petrochemical and utility groups all over the world.
They include ADGAS, AstraZeneca, Bayer, Dow AgroSciences, Novartis,
Pilkington Technology, Unilever and United Utilities. Core
expertise includes LIMS consultancy, regulatory compliance and IS
strategy.
Scimcon’s in-depth practical experience enables it to successfully
tackle the problems that face its clients every day. Customers
appreciate Scimcon’s real-world advice, which helps them set
achievable goals for IS strategy, for LIMS solutions and for
regulatory compliance. For more information contact Natalie Prichard, Citigate Technology, 01604 232223 or email natalie.prichard@citigatetechnology.com
Before joining Scimcon in June 2000 Geoff spent several years with laboratory information system provider Thermo LabSystems. Prior to this Geoff worked as a technical chemist for a detergent company, and with a major technology supplier.
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