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2017 ; 17
(Suppl 1
): 55
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Network mirroring for drug repositioning
#MMPMID28539121
Park S
; Lee DG
; Shin H
BMC Med Inform Decis Mak
2017[May]; 17
(Suppl 1
): 55
PMID28539121
show ga
BACKGROUND: Although drug discoveries can provide meaningful insights and
significant enhancements in pharmaceutical field, the longevity and cost that it
takes can be extensive where the success rate is low. In order to circumvent the
problem, there has been increased interest in 'Drug Repositioning' where one
searches for already approved drugs that have high potential of efficacy when
applied to other diseases. To increase the success rate for drug repositioning,
one considers stepwise screening and experiments based on biological reactions.
Given the amount of drugs and diseases, however, the one-by-one procedure may be
time consuming and expensive. METHODS: In this study, we propose a machine
learning based approach for efficiently selecting candidate diseases and drugs.
We assume that if two diseases are similar, then a drug for one disease can be
effective against the other disease too. For the procedure, we first construct
two disease networks; one with disease-protein association and the other with
disease-drug information. If two networks are dissimilar, in a sense that the
edge distribution of a disease node differ, it indicates high potential for
repositioning new candidate drugs for that disease. The Kullback-Leibler
divergence is employed to measure difference of connections in two constructed
disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked
diseases. RESULTS: The results showed that F-measure of the proposed method was
0.75, outperforming 0.5 of greedy searching for the entire diseases. For the
utility of the proposed method, it was applied to dementia and verified 75%
accuracy for repositioned drugs assuming that there are not any known drugs to be
used for dementia. CONCLUSION: This research has novelty in that it discovers
drugs with high potential of repositioning based on disease networks with the
quantitative measure. Through the study, it is expected to produce profound
insights for possibility of undiscovered drug repositioning.
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