Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.

[1]  Mohieddin Jafari,et al.  PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis , 2015, Database J. Biol. Databases Curation.

[2]  Ruth Nussinov,et al.  Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.

[3]  R. Solé,et al.  Data completeness—the Achilles heel of drug-target networks , 2008, Nature Biotechnology.

[4]  M. Schwab,et al.  Pharmacogenomics: a key component of personalized therapy , 2012, Genome Medicine.

[5]  S. J. Campbell,et al.  Visualizing the drug target landscape. , 2010, Drug discovery today.

[6]  P. Chène,et al.  Drugs Targeting Protein–Protein Interactions , 2006, ChemMedChem.

[7]  F. Sams-Dodd,et al.  Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. , 2013, Drug discovery today.

[8]  Seth I. Berger,et al.  Role of systems pharmacology in understanding drug adverse events , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[9]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[10]  Zhong Wang,et al.  Modular pharmacology: the next paradigm in drug discovery , 2012, Expert opinion on drug discovery.

[11]  J. Nacher,et al.  Modularity in Protein Complex and Drug Interactions Reveals New Polypharmacological Properties , 2012, PloS one.

[12]  R. Solé,et al.  The topology of drug-target interaction networks: implicit dependence on drug properties and target families. , 2009, Molecular bioSystems.

[13]  J. Medina-Franco,et al.  Shifting from the single to the multitarget paradigm in drug discovery. , 2013, Drug discovery today.

[14]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[15]  A. Y. Lu,et al.  Pharmacogenetics, Pharmacogenomics, and Individualized Medicine , 2011, Pharmacological Reviews.

[16]  S. Lampel,et al.  The druggable genome: an update. , 2005, Drug discovery today.

[17]  Wang Bing-Hong,et al.  Network Entropy Based on Topology Configuration and Its Computation to Random Networks , 2008 .

[18]  Afzal R Mohammed,et al.  Application of genomics, proteomics and metabolomics in drug discovery, development and clinic. , 2013, Therapeutic delivery.

[19]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[20]  Sherry L. Jenkins,et al.  Network analysis of FDA approved drugs and their targets. , 2007, The Mount Sinai journal of medicine, New York.

[21]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Thomas Manke,et al.  Robustness and network evolution--an entropic principle , 2005 .

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

[24]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[25]  Soyoung Lee,et al.  Building a drug–target network and its applications , 2009, Expert opinion on drug discovery.

[26]  Jun Zou,et al.  Advanced Systems Biology Methods in Drug Discovery and Translational Biomedicine , 2013, BioMed research international.

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

[28]  S. Kandel,et al.  Role of Protein–Protein Interactions in Cytochrome P450-Mediated Drug Metabolism and Toxicity , 2014, Chemical research in toxicology.

[29]  Dhara Patel,et al.  BCR ABL Kinase Inhibitors for Cancer Therapy , 2010 .

[30]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[31]  David L. Robertson,et al.  JNets: Exploring networks by integrating annotation , 2009, BMC Bioinformatics.

[32]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[33]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[34]  Falk Schreiber,et al.  Analysis of Biological Networks , 2008 .

[35]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[36]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[37]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

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

[39]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[40]  D. Djakiew,et al.  Superior effectiveness of ibuprofen compared with other NSAIDs for reducing the survival of human prostate cancer cells , 2002, Cancer Chemotherapy and Pharmacology.

[41]  Ravi Iyengar,et al.  Network analyses in systems pharmacology , 2009, Bioinform..

[42]  Lourdes Cucurull-Sanchez,et al.  Relevance of systems pharmacology in drug discovery. , 2012, Drug discovery today.

[43]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[44]  Ryoichi Fujiwara,et al.  Extensive protein interactions involving cytochrome P450 3A4: a possible contributor to the formation of a metabolosome , 2014, Pharmacology research & perspectives.

[45]  Sarath Chandra Janga,et al.  Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. , 2009, Molecular bioSystems.

[46]  Chris T. A. Evelo,et al.  CyTargetLinker: A Cytoscape App to Integrate Regulatory Interactions in Network Analysis , 2013, PloS one.

[47]  Hao Liao,et al.  Predicting missing links via correlation between nodes , 2014, ArXiv.

[48]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[49]  John P. Overington,et al.  How many drug targets are there? , 2006, Nature Reviews Drug Discovery.

[50]  R. Nussinov,et al.  Network-based strategies can help mono- and poly-pharmacology drug discovery: a systems biology view. , 2014, Current pharmaceutical design.