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Drug repositioning: computational approaches and research examples

DISCOVERIES (ISSN 2359-7232), 2017, April-June issue


Vogrinc D, Kunej T. Drug repositioning: computational approaches and research examples classified according to the evidence level. Discoveries 2017, Apr-Jun; 5(2): e75. DOI: 10.15190/d.2017.5

Submitted: May 8th, 2017; Revised: June 28th, 2017; Accepted: June 29th, 2017; Published: June 30th, 2017;

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Drug repositioning: computational approaches and research examples classified according to the evidence level

David Vogrinc, Tanja Kunej*

Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Slovenia.

*Corresponding author: Tanja Kunej, PhD, Chair of Genetics, animal biotechnology and immunology, Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, Slovenia, E-mail: tanja.kunej@bf.uni-lj.si 


Increasing need for novel drugs and their application for treating diseases are the main reasons for the development of bioinformatics platforms for drug repositioning. The use of existing approved drugs for treating other diseases reduces cost and time needed for a drug to come to clinical use. Different strategies for drug repositioning have been reported. The use of several omics types is becoming increasingly important in drug repositioning. Although there are several public databases intended for drug repositioning, not many successful cases of novel use of drugs have been reported in the literature and transferred to clinical use. Additionally, the study approaches in published literature are very heterogeneous. A classification scheme - Drug Repositioning Evidence Level (DREL) - for drug repositioning projects, according to the level of scientific evidence has been proposed previously. In the present study, we have reviewed main databases and bioinformatics approaches enabling drug repositioning studies. We also reviewed six published studies and evaluated them according to the DREL classification. The evaluated cases used drug repositioning approach for therapy of rheumatoid arthritis, cancer, coronary artery disease, diabetes, and gulf war illness. The drug repositioning study field could benefit from clearer definition in published articles therefore including drug repositioning DREL classification scheme could be included in published original and review studies. Novel bioinformatics approaches to improve prediction of drug-target interactions, continuous updating of the databases, and development of novel validation techniques are needed to facilitate the development of the drug repositioning field. Although there are still many challenges in drug repositioning and personalized medicine, stratification of patients based on their molecular signatures and testing of signature-targeting drugs should improve drug efficacy in clinical trials.

Access full text of the manuscript here: 


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