We have reported on the application of a computer discovery support tool to identify complementary but disjoint structures within the literature of medical life sciences regarding disease profiles that may benefit from thalidomide. We argued that thalidomide shifts the Th1/Th2 balance toward Th2 in human immune response. In theory, autoimmune diseases that are characterized by a Th1 differentiation may benefit from thalidomide use. Because of the complexity of the processes in the immune system, it was difficult to grasp the molecular changes induced by thalidomide when applied to the large variety of diseases known to date. However, the literature-based discovery support system successfully facilitated the formation of hypotheses regarding some of thalidomide’s mechanistic pathways. The current investigations provided only bibliographic evidence for positive pharmacologic effects of thalidomide in the diseases identified. Experimental and clinical evaluation of therapeutic benefits versus toxicities should shed light on their potential use for the treatment of various pro-inflammatory diseases that have not been studied in this context yet.
As we executed our discovery task in the end of July 2000, we now have two more years of medical research and publications to evaluate our proposed hypotheses. The application of thalidomide as a potential treatment for chronic hepatitis C has been a subject of recent discussion.47
Additionally, it is interesting to observe that one of the reasons of using INFα for treating hepatitis C is the inhibition of INFγ, which is also one of the pharmacologic effects of thalidomide. To complicate matters, however, clinical trials are also being conducted to use INFγ for IFNα nonresponders (see <http:// ClinicalTrials.gov
>). This is a further illustration of the human immune system’s complexity and also suggests careful pre-clinical and clinical testing to truly evaluate the generated hypotheses.
New knowledge about existing drugs is generated continuously. Famous examples of new indications for existing drugs include acetylsalicylic acid (aspirin) as prophylaxis for myocardial infarction and colorectal cancer,48
minoxidil for male pattern baldness,49
and sildenafil (Viagra) for erectile dysfunction.50
The discovery of these new applications originated from clinical observation, theoretical reasoning, and serendipity. With our discovery support system, we have extended the process of drug discovery by generating hypotheses in a systematic manner by combining existing knowledge. This aspect of knowledge re-use may result in a more rapid identification of other potential applications for drugs as well as identification of patterns of side effects, for example.
Working with our tool is more complicated than executing a general PubMed search. It is an intellectually intensive process in which a domain expert has to continually evaluate hypotheses to reduce the long initial list to strong, testable ones. An example of the indispensability of expert knowledge concerns the discovery of myasthenia gravis as target disease for thalidomide. During the analyses, one PubMed citation was found that co-mentioned thalidomide and experimental myasthenia gravis.51
This study, conducted in Lewis rats, did not show any pharmacologic effect of thalidomide. A likely reason for this is the fact that Lewis rats respond in a typical Th1 fashion after immunologic challenge.52
In a system in which the Th1 response prevails, pharmacologic interference at this level may not be sufficient to obtain an effect. The expert knowledge enabled us not to identify this citation as a refutation of our hypothesis.
This example of the human expert’s indispensability is typical to our view of literature-based discovery. Computational tools assist human domain experts in studying the literature and deriving and evaluating novel hypotheses. Using tools such as our system may lead to more focused laboratory experiments and may make new and fruitful connections between scientific disciplines. The scientific environment of biomedical research is characterized by a significant increase of data and information from highly automated experiments and online databases. Intelligent and computer-assisted approaches to sift through this information will become necessary tools on the researcher’s bench in the near future.