The global Pharmaceutical Industry is facing a number of unprecedented challenges including increased use of global partners and CROs in the R&D process; pressures from the FDA and other regulatory agencies and the public for greater rigor in safety assessments; and a need to accelerate new drugs to market. All of these challenges require a rapid shift towards data centricity, increased transparency, and the ability to look for safety related effects, perhaps overlooked, in past work through meta-analysis spanning multiple studies.

It is also becoming increasingly clear that while study data will need to be captured and communicated in vendor-neutral data standards (such as CDISC and HL7), the emergence of new types of biotechnology drugs and industrial products such as nanotechnology that can affect drug safety, and new uses of existing drugs, imply that such standards cannot remain static for long because information will need to be rapidly constituted or re-constituted on demand. Pharma R&D organizations and Regulatory Agencies alike must be able to semantically identify and convert data from one or more source formats into a current version of a data standard. This need is clear and present. Publishing raw or structured data in paper formats and PDFs are huge impediments to this important change. Likewise, trends towards designing infrastructure based on standards alone are simply not sustainable.

This whitepaper identifies key drivers for change in current data management practices.