Discovering Complex Relationships of Drugs over Distributed Knowledgebases

Discovering Complex Relationships of Drugs over Distributed Knowledgebases

Juan Li (North Dakota State University, Fargo, ND, USA), Ranjana Sharma (North Dakota State University, Fargo, ND, USA) and Yan Bai (University of Washington Tacoma, Tacoma, WA, USA)
Copyright: © 2014 |Pages: 18
DOI: 10.4018/ijdst.2014010102
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Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.
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Drug discovery is a process of discovering and designing drugs. It is generally related to the fields of medicine, pharmacology and biotechnology. Despite the advances in chemical synthesis techniques as well as combinatorial and cheminformatics and understanding of biological systems, drug discovery is still a lengthy, expensive, and difficult process with a low rate of new therapeutic discovery. The process of drug development consists of drug compounds proposal, pre-clinical, clinical trial, and FDA review and approval (Anyanwu & Sheth, 2003). Usually, 5000 to 10,000 compounds are proposed for a potential drug (DiMasi et al., 2003; DiMasi et al., 1991; Masia 2009; Collier, 2009). An extensive research of the proposed compounds is conducted to select 2.5% to 5% for preclinical trials to be tested on animals. Among the selected compounds for preclinical trial, only 2% may get approved for clinical trial. Finally, only 1 compound becomes an approved drug for the treatment of diseases and use on humans (Collier, 2009). Studies also show that it takes about 15 years from compound proposal to FDA approval of a new drug to treat a disease, and the total cost is between 0.8 to 1 billion US dollars (DiMasi, 2003).

To overcome the aforementioned problems of drug discovery, drug repositioning (Sleigh & Barton, 2010) has been proposed. Drug repositioning is the application of the existing drugs to new indications or new diseases. An existing drug has passed significant pre-clinical and clinical tests. Its toxicity and other effects are already known. Hence, the cost of using it for some other diseases will be much less as compared to developing a drug from scratch (Hopkins, 2008; Boran & Iyengar, 2010; Druker et al., 1996). Conventional drug design follows the principle of “one gene, one disease, one drug”. One drug is targeted for the treatment of one disease caused by one gene (Sleigh & Barton 2010). Unlike conventional drug design, drug repositioning studies interactions of drugs with multiple targets (Hopkins, 2008; Boran & Iyengar, 2010). One drug can be used with multiple diseases. Some repositioned drugs have been approved with new uses that are different from original uses (Chong & Sullivan, 2007; Verma et al., 2005). Here are some examples. Thalidomide normally as a drug for sedation, nausea and insomnia is being used in the treatment of multiple myloma (Durk, 2006). Acetylsalicylic acid, as a drug for reducing aches and pains and fever is being used in cardiology to prevent heart attacks, strokes and blood clotting due to its antiplatelet activity (Krumholz et al., 1995). Also Miltefosine for the treatment of Cancer has the new indication as Visceral leishmania (Sundar et al., 1998). Another very common example is of Sildenafil which was earlier used for hypertensionis now being used for male erectile dysfunction in the name of Viagra (Boolell et al., 1996).

Presently, most of repositioned drugs are developed by observing the side-effects of other drugs (Aronson, 2007; Ashburn &Thor, 2004). However, it will be more effective to study the polypharmacological action of drugs and examine proteins, genes, pathway and other important factors, rather than discovering the effects merely by observation. The hidden relationship between drugs and diseases could not be observed and identified incipiently, so does the information about the drug and its new applications.

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