The transformation and future trends of laboratory data management - Pharmaceutical Technology Asia Pacific
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The transformation and future trends of laboratory data management
Thermo Fisher Scientific reflects on the past few decades of laboratory information management systems and how current market and industry trends are transforming the impact of laboratory data management technology in the future. This article discusses data management systems, from their earliest forms and purpose to how they are being used today, and predicts the trends in technology that will impact the pharmaceutical industry for the next 10 years.


Pharmaceutical Technology Asia Pacific

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The emergence of LIMS

Historically, the main purpose of laboratory information management systems (LIMS) has been to track and manage samples in the laboratory. LIMS originated nearly 30 years ago as a rudimentary method of automating manual, error-prone processes in the laboratory and, with the growth in adoption of technology, became the de facto benchmark for laboratory control and management.



In the early evolution of laboratory information systems as data management tools, LIMS were essentially developed in-house by scientists to satisfy a defined need. Those early LIMS were eventually brought to market by vendors to fulfil a growing need in the industry. While the emergence of LIMS sprang from the environmental industry, the pharmaceutical industry was an early adopter as a result of growing regulatory pressures; increased sample throughput; demand for more efficient management; the need to eliminate human error; and a drive for integrating data throughout the enterprise.

Evolution of pharma LIMS

LIMS were readily adopted in the pharmaceutical industry because they increasingly met industry-specific requirements, in place of organization-specific requirements. The need for organizations to comply with regulatory and manufacturing processes such as cGMP, NAMAS (National Accredititation of Measuring and Sampling), Environmental Protection Agency (EPA), FDA and GLP/GALP was a driving factor in the pharmaceutical industry's early acceptance of LIMS.


Selecting a LIMS — a historical perspective
However, with time many early users were left with heavily customized legacy systems that proved difficult to upgrade and integrate with other business functions. As the industry itself changed, and merger and acquisition (M&A) activity altered the pharmaceutical landscape, application integration became a necessity to ensure that laboratories made their data accessible throughout the enterprise. After 10–15 years, homegrown legacy systems had become almost obsolete — replaced by more available, upgradeable and compliant vendor-supplied generic LIMS. These early systems, while designed to provide basic data management and meet regulatory compliance, required a high degree of customization to meet the specific needs of each particular user group across the enterprise. LIMS were not customarily designed to operate at full functionality, which required pharma users at the research, development or manufacturing phase of drug development to 'customize' the LIMS for that particular laboratory application with its specific workflow and data management requirements.

Past technology trends

Technology has played an important role in the changing face of LIMS. In-house LIMS were typically based on simple spreadsheet packages, while vendor-developed LIMS were frequently modelled on minicomputer platforms, such as Digital's VAX running the VMS or UNIX operating systems. These LIMS had the advantage of being multiuser, secure and centrally administered. Increasingly, business rules such as operator approvals, test schedules, templates and the core database became centralized server-based activities. Against this background ran the contra-argument for PC-based integrator systems, which claimed that support costs for minicomputers and associated software were more costly.


Current trends (2007)
PC-based solutions were then based on the basic Windows platform on top of a DOS operating system. Disadvantages were lack of security, the need to copy methods onto all machines individually rather than centrally when alterations were required, and a relative lack of processing power (PCs were costly 10 years ago, and so inevitably many organizations consigned their slowest PCs to the task of laboratory data collection and analysis — which up to that point had been done manually).


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29 August 2008
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