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Semantic Web: Safety and Innovation

By Scott Lundstrom

Oct 17, 2005 | The development of Semantic Web capabilities provides exciting opportunities for life science companies to address two major challenges confronting them — innovation and safety — improvements in which require data integration among myriad data sources. (See Masters of the Semantic Web, page 28.)

There are several ways that Semantic Web technologies can be implemented within a biopharmaceutical R&D organization. Semantic Web deployments are highly flexible and can range from small departmental solutions to large multi-enterprise consortium. At each organizational level, informatics developers can create significant value through the application of Semantic Web technology. As standards emerge and companies become more experienced in deploying the technology, we expect to see many valuable capabilities developed:

  • Knowledge-based applications that allow reasoning and inference to suggest experimental direction and enable true hypothesis-based drug discovery
  • Alliance management systems allowing knowledge and IP to flow securely between organizations
  • Semantic integration of data across companies, business units, and external sites
  • Safety-signal detection systems for pharmacovigilance and pharmacogenomics
  • Mining of intellectual property residing in data warehouses, spreadsheets, presentations, and publications

As the COX-2 inhibitor debacle makes painfully clear, biopharma companies need new methods and systems for tracking toxicity indicators in defined patient populations earlier in the pipeline. Pharmacovigilance is essentially a problem of data standardization, integration, signal detection, and reporting. R&D generates huge volumes of experimental data that eventually become part of the legacy of a compound’s IND filing. Much of this information might signal potential toxicities for compounds further up the value chain, but traditional analytic tools have typically failed to identify potential clinical adverse events. Specific issues and needs include:

  • Connecting and interpreting information across all R&D business units
  • Implementing safety signal detection systems throughout the entire drug discovery pipeline
  • Creating an IT and informatics architecture that enables true safety signal detection

Addressing these needs improves competitive intelligence and surveillance, relates patient profiles and genomic data to safety, enhances risk-benefit assessment to drive preferred treatment plans, and provides continued education and alerts to reduce avoidable drug reactions.

Deploying Semantic Web methodologies for data integration and creating intelligent applications for signal detection helps researchers in preclinical and clinical development search and view the research history of a compound. As a result, R&D CIOs could enable the creation of overarching views and predictions, leading to safer therapeutics in the marketplace.

Semantic Innovation
Biopharma and biotech companies need new systems for interpreting experimental data to improve R&D productivity. The quantity of data and IT architecture is too much for any researcher to completely interpret his or her results. A strategically designed knowledge architecture allows the creation of applications to generate testable scientific hypotheses, thus leading to new insights about targets, compounds, and diseases — the essence of hypothesis-driven drug discovery.

A Semantic Web approach could include modifying semantic architectures built for other vertical markets such as the Department of Defense and Intelligence Community to advise combat commanders and adapting them for the pharmaceutical industry’s need for more intelligent decision support systems.

Semantic technologies are still developing, and the market includes many niche categories that may be required to develop a complete system. Companies implementing such technologies need to balance value and complexity in assembling a solution. Semantic Web technologies will create opportunities for improvements in discovery, development, and safety. Companies should move aggressively to pilot the use of semantic technologies in these areas.

Scott Lundstrom is VP Research, Life Science Insights. E-mail:

Related article:
Berners-Lee Seeks Killer App for Semantic Web

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