Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide.
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.
Weeber
Marc
M
Department of Social Pharmacy and Pharmacoepidemiology, Groningen University Institute for Drug Exploration, The Netherlands. marc@weeber.net.
Vos
Rein
R
Klein
Henny
H
De Jong-Van Den Berg
Lolkje T W
LT
Aronson
Alan R
AR
Molema
Grietje
G
D016206:Databases, Bibliographic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D018664:Interleukin-12 / Q000037:antagonists & inhibitors; D009323:Natural Language Processing; D039781:PubMed; D013792:Thalidomide / Q000276:immunology / Q000627:therapeutic use*; D017432:Unified Medical Language System
D018664:Interleukin-12; D013792:Thalidomide