Pathway-based drug repositioning using causal inference

BackgroundRecent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs.MethodsUnlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer).ResultsTo demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease.ConclusionsWe successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for drug repositioning.

[1]  Donald A. Wilson,et al.  ApoE-Directed Therapeutics Rapidly Clear β-Amyloid and Reverse Deficits in AD Mouse Models , 2012, Science.

[2]  Jennifer Mulle,et al.  A Genome-Wide Scan of Ashkenazi Jewish Crohn's Disease Suggests Novel Susceptibility Loci , 2012, PLoS genetics.

[3]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[4]  Taebo Sim,et al.  Chemical kinomics: a powerful strategy for target deconvolution. , 2010, BMB reports.

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

[6]  Anton Yuryev,et al.  Computational Approaches for Drug Repositioning and Combination Therapy Design , 2010, J. Bioinform. Comput. Biol..

[7]  Gary D Bader,et al.  BioPAX – A community standard for pathway data sharing , 2010, Nature Biotechnology.

[8]  S. Friend,et al.  A network view of disease and compound screening , 2009, Nature Reviews Drug Discovery.

[9]  Kei-Hoi Cheung,et al.  BioPAX – A community standard for pathway data sharing , 2010, Nature Biotechnology.

[10]  W. Strittmatter,et al.  Old Drug, New Hope for Alzheimer's Disease , 2012, Science.

[11]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[12]  Guanghui Hu,et al.  Human Disease-Drug Network Based on Genomic Expression Profiles , 2009, PloS one.

[13]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[14]  C Joachim,et al.  Controlled clockwise and anticlockwise rotational switching of a molecular motor. , 2013, Nature nanotechnology.

[15]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[16]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[17]  Judy H Cho,et al.  Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis , 2007, Nature Genetics.

[18]  Alexander A. Morgan,et al.  Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data , 2011, Science Translational Medicine.

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

[20]  Pankaj Agarwal,et al.  A Pathway-Based View of Human Diseases and Disease Relationships , 2009, PloS one.

[21]  H. Johnen,et al.  IL-1 receptor antagonist anakinra enhances tumour growth inhibition in mice receiving peptide vaccination and beta-(1-3),(1-6)-D-glucan. , 2010, Anticancer research.

[22]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[23]  Alexander A. Morgan,et al.  Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease , 2011, Science Translational Medicine.

[24]  A. Butte,et al.  Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks , 2010, Journal of cardiovascular translational research.

[25]  Corey Nislow,et al.  Bugs, drugs and chemical genomics. , 2011, Nature chemical biology.

[26]  Anton Yuryev,et al.  Pathway analysis for design of promiscuous drugs and selective drug mixtures. , 2006, Current drug discovery technologies.

[27]  Chris Sander,et al.  Discovering modulators of gene expression , 2010, Nucleic acids research.

[28]  E. Loftus.  Clinical epidemiology of inflammatory bowel disease: Incidence, prevalence, and environmental influences. , 2004, Gastroenterology.

[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]  J. Scannell,et al.  Diagnosing the decline in pharmaceutical R&D efficiency , 2012, Nature Reviews Drug Discovery.

[31]  Yajie Wang,et al.  Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer , 2012, PLoS Comput. Biol..

[32]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[33]  Thomas C. Wiegers,et al.  Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks , 2008, Nucleic Acids Res..