Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report of a Search for New Potential Therapeutic Uses for Thalidomide
- Marc Weeber, PhD,
- Rein Vos, MD, PhD,
- Henny Klein, PhD,
- Lolkje T W de Jong-van den Berg, PhD,
- Alan R Aronson, PhD,
- Grietje Molema, PhD
- Affiliation of the authors: Department of Social Pharmacy and Pharmacoepidemiology, Groningen University Institute for Drug Exploration, The Netherlands (MW, HK, LTWdJ-vdB) Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland (MW, ARA) HEALTH Research Institute, Department of Health Ethics and Philosophy, University of Maastricht, The Netherlands (RV) Department of Pathology and Laboratory Medicine, Medical Biology Section, Groningen University Institute for Drug Exploration, The Netherlands (GM)
- Correspondence and reprints: Marc Weeber, PhD, Department of Medical Informatics, Erasmus MC, Erasmus University Rotterdam, The Netherlands; e-mail: <marc{at}weeber.net>.
- Received 13 May 2002
- Accepted 3 January 2003
Abstract
The availability of scientific bibliographies through online databases provides a rich source of information for scientists to support their research. However, the risk of this pervasive availability is that an individual researcher may fail to find relevant information that is outside the direct scope of interest. Following Swanson's ABC model of disjoint but complementary structures in the biomedical literature, we have developed a discovery support tool to systematically analyze the scientific literature in order to generate novel and plausible hypotheses. In this case report, we employ the system to find potentially new target diseases for the drug thalidomide. We find solid bibliographic evidence suggesting that thalidomide might be useful for treating acute pancreatitis, chronic hepatitis C, Helicobacter pylori-induced gastritis, and myasthenia gravis. However, experimental and clinical evaluation is needed to validate these hypotheses and to assess the trade-off between therapeutic benefits and toxicities.
Footnotes
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The authors are grateful to James G. Mork from the National Library of Medicine for providing access to the NLP tools.
