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Knowledge Management Tools for Drug Discovery
Decision Resources, Inc., June 2005, Pages: 14
Pharmaceutical companies are investing in new R&D technologies in the hope that they will accelerate research and, ultimately, improve both productivity and profitability. Before these technologies can boost profitability, however, the data they produce must be converted into knowledge that can be applied to the development of new drugs. A significant bottleneck in this process is the long practiced, but inefficient, method that scientists use to integrate new data and information with existing knowledge--i.e., the paper-based notebook.
In this report, we examine how knowledge management principles can be used to identify and apply computational tools, including electronic laboratory notebooks and knowledgebases, to synthesize scientific knowledge.
Business Implications
- Investment in data-intensive technologies has contributed significantly to the precipitous rise in pharmaceutical R&D costs, but it has not increased the number of new drug launches. A significant bottleneck in deriving value from data produced by these technologies is the inefficient method that scientists use to integrate new data with existing knowledge. Computational knowledge management tools can aid researchers in accomplishing this task more efficiently.
- A primary source of existing knowledge in drug discovery organizations is laboratory notebooks. Retrieving information from paper-based notebooks is a clear bottleneck in generating new knowledge. Because of their searchability, electronic laboratory notebooks (ELNs) will enhance knowledge utilization. User adoption is one of the main hurdles to implementing ELNs; developers must therefore address concerns such as flexibility, ease of use, privacy, and authentication.
- Integration of information from scientific literature is another bottleneck on the path from data to knowledge. Assistance in identifying relevant literature and integrating it with analyses should improve the efficiency of decision making. Model-oriented knowledgebases (KBs) can accelerate the drug discovery process by reducing the time spent on these processes.
- Recent generations of ELNs interface with most data-generating instrumentation and data warehouses. These ELNs may eventually provide single-interface access to both data and knowledge. We expect that ELNs will evolve into portals that scientists can access to conduct research activity.
- Although model-oriented KBs are currently used for target identification and validation, researchers are likely to find high-value uses in areas such as lead compound selection, toxicology, and clinical development. These uses may help companies reduce clinical failure rates and expand indications for existing approved drugs.
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