Abstract
BackgroundEfficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.ResultsIn this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.ConclusionsOur method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.
Highlights
Efficient identification of drug targets is one of major challenges for drug discovery and drug development
Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks
Raman et al constructed a comprehensive model of mycolic acid synthesis metabolic pathway in the pathogen Mycobacterium tuberculosis and used flux balance analysis (FBA) to do in silico systematic gene deletions which identify proteins essential for this pathway and lead to identification of anti-tubercular drug targets [18]
Summary
Efficient identification of drug targets is one of major challenges for drug discovery and drug development. If components other than disease-causing compounds are affected by a drug, toxicity or side effect will arise; on the other hand, if disease-causing compounds are not inhibited by a drug, lack of efficacy will arise Both of these problems have been attributed to sloppy early target discovery and are among the main challenges in developing new drugs. With the complete sequencing of human and bacterial genomes and the subsequent accumulation of genomic, proteomic, and metabolomic data, systems biology approaches or network-based analyses hold great promise for identifying drug targets by utilizing biological networks, such as gene regulatory networks, metabolic networks and protein interaction networks [5,6,7,8,9,10,11,12,13,14,15,16]. Wealth of various types of omics data are changing the way researchers view drug targets and provides unprecedent opportunities for drug target identification
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