Mining Biomedical Literature and Ontologies for Drug Repositioning Discovery

Drug development is time-consuming, costly, and risky. Approximate 80% to 90% of drug development projects fail before they ever get into clinical trials. To reduce the high risk of failure for drug development, pharmaceutical companies are exploring the drug repositioning approach for drug development. Previous studies have shown the feasibility of using computational methods to help extract plausible drug repositioning candidates, but they all encountered some limitations. In this study, we propose a novel drug-repositioning discovery method that takes into account multiple information sources, including more than 18,000,000 biomedical research articles and some existing ontologies that cover detailed relations between drugs, proteins and diseases. We design two experiments to evaluate our proposed drug repositioning discovery method. Overall, our evaluation results demonstrate the capability and superiority of our proposed drug repositioning method for discovering potential, novel drug-disease relationships.

[1]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[2]  Hyunju Lee,et al.  Predicting Drug-Target Interactions Using Drug-Drug Interactions , 2013, PloS one.

[3]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.

[4]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[5]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2013 , 2012, Nucleic Acids Res..

[6]  Doheon Lee,et al.  Discovering context-specific relationships from biological literature by using multi-level context terms , 2012, BMC Medical Informatics and Decision Making.

[7]  Trent D. Stephens,et al.  Dark Remedy: The Impact of Thalidomide and Its Revival as a Vital Medicine , 2000 .

[8]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Sunghoon Kim,et al.  Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug , 2012, BMC Systems Biology.

[10]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[11]  Neil R. Smalheiser,et al.  Artificial Intelligence An interactive system for finding complementary literatures : a stimulus to scientific discovery , 1995 .

[12]  Xiaoyan Zhu,et al.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts , 2009, PLoS Comput. Biol..

[13]  Pankaj Agarwal,et al.  Systematic Drug Repositioning Based on Clinical Side-Effects , 2011, PloS one.

[14]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[15]  Marc Weeber,et al.  Using concepts in literature-based discovery: simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries , 2001 .

[16]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[17]  Jacob de Vlieg,et al.  Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases , 2010, PLoS Comput. Biol..

[18]  Jonathan D. Wren,et al.  Knowledge discovery by automated identification and ranking of implicit relationships , 2004, Bioinform..

[19]  W. Mcbride,et al.  Thalidomide and Congenital Abnormalities , 1961 .

[20]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[21]  Eric K. Neumann,et al.  Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships , 2009, BMC Bioinformatics.

[22]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[23]  Alexander E. Ivliev,et al.  Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach , 2013, PloS one.

[24]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2011 , 2010, Nucleic Acids Res..

[25]  Wanda Pratt,et al.  A new evaluation methodology for literature-based discovery systems , 2009, J. Biomed. Informatics.

[26]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[27]  Van V. Brantner,et al.  Estimating the cost of new drug development: is it really 802 million dollars? , 2006, Health affairs.

[28]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[29]  Zhiyong Lu,et al.  A new method for computational drug repositioning using drug pairwise similarity , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[30]  Luonan Chen,et al.  Network-based drug repositioning. , 2013, Molecular bioSystems.