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Systematic Approach for Analyzing Drug Combination by Using Target-Enzyme Distance

  • Park, Jaesub (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Sunjae (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Kiseong (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Doheon (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2013.04.22
  • Accepted : 2013.04.23
  • Published : 2013.06.28

Abstract

Recently, the productivity of drug discovery has gradually decreased as the limitations of single-target-based drugs for various and complex diseases become exposed. To overcome these limitations, drug combinations have been proposed, and great efforts have been made to predict efficacious drug combinations by statistical methods using drug databases. However, previous methods which did not take into account biological networks are insufficient for elaborate predictions. Also, increased evidences to support the fact that drug effects are closely related to metabolic enzymes suggested the possibility for a new approach to the study drug combinations. Therefore, in this paper we suggest a novel approach for analyzing drug combinations using a metabolic network in a systematic manner. The influence of a drug on the metabolic network is described using the distance between the drug target and an enzyme. Target-enzyme distances are converted into influence scores, and from these scores we calculated the correlations between drugs. The result shows that the influence score derived from the targetenzyme distance reflects the mechanism of drug action onto the metabolic network properly. In an analysis of the correlation score distribution, efficacious drug combinations tended to have low correlation scores, and this tendency corresponded to the known properties of the drug combinations. These facts suggest that our approach is useful for prediction drug combinations with an advanced understanding of drug mechanisms.

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