The Food and Drug Administration's (FDA) Critical Path Initiative and subsequent guidance documents, such as "Pharmaceutical
GMPs for the 21st Century and Quality Systems Approach to Pharmaceutical Current Good Manufacturing Practice Regulations,"
have sparked the revitalization of the life sciences industry. FDA's direction provides a framework to accelerate the development
of safe and effective medical products and bring these products to market more quickly, thereby significantly improving the
quality of health care. The Critical Path Initiative focuses on the applied science within essential areas that impact this
objective, specifically including safety, efficacy, and manufacturing.
In recent years, increased complexity in drug development has resulted in fewer products coming to market. The Critical Path
Initiative suggests a re-evaluation of conventional business processes and a focus on information technology (IT) as an enabler.
One factor is consistent—the need for business-critical information within IT systems agile enough to conform to the evolving
business process requirements. From data mining and predictive modeling to manufacturing quality systems, an agile, robust
IT infrastructure will allow the life sciences industry to achieve many of the goals set forth by the Critical Path Initiative.
As the life sciences industry begins to improve business processes to address its myriad challenges, access to credible and
relevant business information is critical and will lead to increasing data integration efficiency from preclinical and early
stage discovery. Many of the data integration challenges in drug discovery are a result of fast-paced advancement in both
science and technology over the last couple decades. The advent of automated gene sequencing, expression analysis, combinatorial
chemistry, and ultra-high throughput screening has contributed to the complexity of data integration. Although many good integration
strategies exist, interest has shifted from physical data integration to new approaches, techniques, methods, and algorithms
for performing semantic data integration.
Semantics can be used to ensure that two concepts found in different forms in different data sources actually describe the
same object. The life sciences community is expressing interest in the Semantic Web because it promises to assemble information
into useful blocks of knowledge. For example, it can functionally link gene definitions to inherited diseases and can interweave
their positions within a biochemical pathway. The Semantic Web holds great promise.
Additionally, one of the most advanced areas in which IT can certainly improve the efficiency of discovery is in systems biology,
which involves using complex analytical techniques to find novel patterns and insights. The life sciences industry is using
IT to help generate new insights, making discovery more effective. Further, systems biology and the Semantic Web support the
translational research approach indicated in the Critical Path Initiative.
As these technologies address the needs within discovery, similar interoperability requirements exist for clinical development.
The integration of patient clinical information from clinical investigator sites and sponsors has the potential to advance
the overall clinical development process and improve the likelihood for success. Here, the life sciences industry must work
with IT providers to develop solutions using an industry standards-based approach (e.g., HL7 V3) as part of an overall electronic
medical record. The integration and accessibility of patient clinical information and clinical trial management systems will
serve as an important component in closing the gap between laboratory bench discovery and bedside application.
In addition to enabling patient-centric clinical information, IT solutions must also integrate with manufacturing processes
to ensure the delivery of high-quality medical products. Improving manufacturing efficiencies, the third area of focus of
the Critical Path Initiative, will require flexible and agile solutions that can easily adapt to the latest quality systems
requirements. These IT solutions must go beyond conventional supply chain management requirements to automate a variety of
compliance needs and provide critical information that enables more effective business decisions. The implementation of product
data hubs will play an important role in enabling life sciences companies to provide safe, effective medical products in a
more timely and cost effective way.