In silico methods to address polypharmacology: Current status, applications and future perspectives

Drug Discovery Today

Volumn 21, Issue 2, 2016, Pages 288-298

  Lavecchia, Antonio ^a   Cerchia, Carmen ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ASTRAGALOSIDE IV; LIGAND;

ALGORITHM; BINDING SITE; COMPUTER MODEL; DRUG DESIGN; DRUG REPOSITIONING; DRUG RESPONSE; DRUG STRUCTURE; MOLECULAR DOCKING; PHENOTYPE; POLYPHARMACOLOGY; REVIEW; ANIMAL; COMPUTER SIMULATION; DRUG DEVELOPMENT; HUMAN; MOLECULAR MODEL; STRUCTURE ACTIVITY RELATION;

ANIMALS; BINDING SITES; COMPUTER SIMULATION; DRUG DISCOVERY; HUMANS; LIGANDS; MODELS, MOLECULAR; POLYPHARMACOLOGY; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84953329262     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2015.12.007     Document Type: Review

References (113)

1. A.L. Hopkins Network pharmacology: the next paradigm in drug discovery Nat. Chem. Biol. 4 2008 682 690
2. M.H. Van Regenmortel Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism EMBO Rep. 5 2004 1016 1020
3. G.V. Paolini, and et al. Global mapping of pharmacological space Nat. Biotechnol. 24 2006 805 815
4. A.D. Boran, and R. Iyengar Systems approaches to polypharmacology and drug discovery Curr. Opin. Drug Discov. Dev. 13 2010 297 309
5. X. Jalencas, and J. Mestres On the origins of drug polypharmacology MedChemComm 4 2013 80 87
6. C.R. Chong, and D.J. Sullivan Jr. New uses for old drugs Nature 448 2007 645 646
7. T.I. Oprea, and et al. Drug repurposing from an academic perspective Drug Discov. Today: Ther. Strateg. 8 2011 61 69
8. T.T. Ashburn, and K.B. Thor Drug repositioning: identifying and developing new uses for existing drugs Nat. Rev. Drug Discov. 3 2004 673 683
9. A.F. Shaughnessy Old drugs, new tricks BMJ 342 2011 d741
10. T.I. Oprea, and J. Mestres Drug repurposing: far beyond new targets for old drugs AAPS J. 14 2012 759 763
11. M. Allarakhia Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases Drug Des. Dev. Ther. 7 2013 753 766
12. T. Langer, and C.-G. Wermuth Selective optimization of side activities (SOSA): a promising way for drug discovery J.-U. Peters, Polypharmacology in Drug Discovery 2012 John Wiley & Sons 227 243
13. J.U. Peters Polypharmacology - foe or friend? J. Med. Chem. 56 2013 8955 8971
14. T. Klabunde Chemogenomic approaches to drug discovery: similar receptors bind similar ligands Br. J. Pharmacol. 152 2007 5 7
15. A. Masoudi-Nejad, and et al. Drug-target and disease networks: polypharmacology in the post-genomic era In Silico Pharmacol. 1 2013 17
16. M. Duran-Frigola, and et al. Structural systems pharmacology: the role of 3D structures in next-generation drug development Chem. Biol. 20 2013 674 684
17. M. Campillos, and et al. Drug target identification using side-effect similarity Science 321 2008 263 266
18. F. Cheng, and et al. Prediction of drug-target interactions and drug repositioning via network-based inference PLoS Comput. Biol. 8 2012 e1002503
19. N. Chandra, and J. Padiadpu Network approaches to drug discovery Expert. Opin. Drug Discov. 8 2013 7 20
20. M. Johnson, and G.M. Maggiora Concepts and Applications of Molecular Similarity 1990 John Wiley & Sons
21. X. Liu, and et al. In silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion J. Cheminform. 6 2014 33
22. L.H. Mervin, and et al. Target prediction utilising negative bioactivity data covering large chemical space J. Cheminform. 7 2015 51
23. S. Ekins, and et al. Dispensing processes impact apparent biological activity as determined by computational and statistical analyses PLoS One 8 2013 e62325
24. F. Prinz, and et al. Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 10 2011 712
25. D. Fourches, and et al. Curation of chemogenomics data Nat. Chem. Biol. 11 2015 535
26. A. Gonzalez, and P.R. Peres-Neto Data curation: act to staunch loss of research data Nature 520 2015 436
27. A. Lavecchia Machine-learning approaches in drug discovery: methods and applications Drug Discov. Today 20 2015 318 331
28. D. Rognan Towards the next generation of computational chemogenomics tools Mol. Inform. 32 2013 1029 1034
29. E. Gregori-Puigjane, and J. Mestres Coverage and bias in chemical library design Curr. Opin. Chem. Biol. 12 2008 359 365
30. A. Koutsoukas, and et al. From in silico target prediction to multi-target drug design: current databases, methods and applications J. Proteomics 74 2011 2554 2574
31. M.J. Keiser, and et al. Relating protein pharmacology by ligand chemistry Nat. Biotechnol. 25 2007 197 206
32. E. Lounkine, and et al. Large-scale prediction and testing of drug activity on side-effect targets Nature 486 2012 361 367
33. D. Reker, and et al. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus Proc. Natl. Acad. Sci. U. S. A. 111 2014 4067 4072
34. D. Reker, and et al. Revealing the macromolecular targets of complex natural products Nat. Chem. 6 2014 1072 1078
35. M. Reutlinger, and et al. Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands Angew. Chem. Int. Ed. Engl. 53 2014 582 585
36. P. Willett Similarity methods in chemoinformatics Ann. Rev. Inf. Sci. Technol. 43 2009 1 117
37. J.D. MacCuish, and N.E. MacCuish Chemoinformatics applications of cluster analysis Wiley Interdiscip. Rev. Comput. Mol. Sci. 4 2014 34 48
38. O. Mendez-Lucio, and et al. Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context ChemMedChem 9 2014 560 565
39. S.R. Vasudevan, and et al. Shape-based reprofiling of FDA-approved drugs for the H(1) histamine receptor J. Med. Chem. 55 2012 7054 7060
40. M.D. AbdulHameed, and et al. Exploring polypharmacology using a ROCS-based target fishing approach J. Chem. Inf. Model. 52 2012 492 505
41. V.I. Perez-Nueno, and et al. Detecting drug promiscuity using Gaussian ensemble screening J. Chem. Inf. Model. 52 2012 1948 1961
42. V.I. Perez-Nueno, and et al. GES polypharmacology fingerprints: a novel approach for drug repositioning J. Chem. Inf. Model. 54 2014 720 734
43. J.L. Jenkins, and et al. A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes J. Med. Chem. 47 2004 6144 6159
44. J.H. Nettles, and et al. Flexible 3D pharmacophores as descriptors of dynamic biological space J. Mol. Graph. Model. 26 2007 622 633
45. J.H. Nettles, and et al. Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors J. Med. Chem. 49 2006 6802 6810
46. E.R. Yera, and et al. Chemical structural novelty: on-targets and off-targets J. Med. Chem. 54 2011 6771 6785
47. A. Lavecchia, and C. Di Giovanni Virtual screening strategies in drug discovery: a critical review Curr. Med. Chem. 20 2013 2839 2860
48. D. Rognan Structure-based approaches to target fishing and ligand profiling Mol. Inform. 29 2010 176 187
49. Y.Z. Chen, and D.G. Zhi Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule Proteins 43 2001 217 226
50. H. Li, and et al. TarFisDock: a web server for identifying drug targets with docking approach Nucleic Acids Res. 34 2006 W219 W224
51. J. Zhao, and et al. Therapeutic effects of astragaloside IV on myocardial injuries: multi-target identification and network analysis PLoS One 7 2012 e44938
52. C. Lv, and et al. Ophiobolin O isolated from Aspergillus ustus induces G1 arrest of MCF-7 cells through interaction with AKT/GSK3beta/cyclin D1 signaling Mar. Drugs 13 2015 431 443
53. Z. Gao, and et al. PDTD: a web-accessible protein database for drug target identification BMC Bioinformatics 9 2008 104
54. B.K. Shoichet, and I.D. Kuntz Matching chemistry and shape in molecular docking Protein Eng. 6 1993 723 732
55. S. Eric, and et al. Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity Bioorg. Med. Chem. 20 2012 5220 5228
56. O. Trott, and A.J. Olson AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J. Comput. Chem. 31 2010 455 461
57. M. Scrima, and et al. Structural evidence of N6-isopentenyladenosine as a new ligand of farnesyl pyrophosphate synthase J. Med. Chem. 57 2014 7798 7803
58. R.A. Friesner, and et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 47 2004 1739 1749
59. W. Wang, and et al. The interprotein scoring noises in glide docking scores Proteins 80 2012 169 183
60. K.T. Schomburg, and et al. Facing the challenges of structure-based target prediction by inverse virtual screening J. Chem. Inf. Model. 54 2014 1676 1686
61. E. Kellenberger, and et al. sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank J. Chem. Inf. Model. 46 2006 717 727
62. S.R. Ellingson, and et al. VinaMPI: facilitating multiple receptor high-throughput virtual docking on high-performance computers J. Comput. Chem. 34 2013 2212 2221
63. D. Schuster 3D pharmacophores as tools for activity profiling Drug Discov. Today: Technol. 7 2011 e205 e211
64. U. Grienke, and et al. Accessing biological actions of Ganoderma secondary metabolites by in silico profiling Phytochemistry 114 2015 114 124
65. J.M. Rollinger, and et al. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens Planta Med. 75 2009 195 204
66. X. Liu, and et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach Nucleic Acids Res. 38 2010 W609 W614
67. J. Meslamani, and et al. Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling J. Chem. Inf. Model. 52 2012 943 955
68. X. Jalencas, and J. Mestres Identification of similar binding sites to detect distant polypharmacology Mol. Inform. 32 2013 976 990
69. J. Desaphy, and et al. Encoding protein-ligand interaction patterns in fingerprints and graphs J. Chem. Inf. Model. 53 2013 623 637
70. M. Baroni, and et al. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application J. Chem. Inf. Model. 47 2007 279 294
71. F. Milletti, and A. Vulpetti Predicting polypharmacology by binding site similarity: from kinases to the protein universe J. Chem. Inf. Model. 50 2010 1418 1431
72. L. Xie, and P.E. Bourne Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments Proc. Natl. Acad. Sci. U. S. A. 105 2008 5441 5446
73. L. Siragusa, and et al. BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity Proteins 83 2015 517 532
74. J. Ren, and et al. SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison Nucleic Acids Res. 38 2010 W441 W444
75. M. Arooj, and et al. Finding off-targets, biological pathways, and target diseases for chymase inhibitors via structure-based systems biology approach Proteins 83 2015 1209 1224
76. M. Brylinski, and J. Skolnick A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation Proc. Natl. Acad. Sci. U. S. A. 105 2008 129 134
77. G. Hu, and et al. Human structural proteome-wide characterization of cyclosporine A targets Bioinformatics 30 2014 3561 3566
78. V.J. Haupt, and et al. Drug promiscuity in PDB: protein binding site similarity is key PLoS One 8 2013 e65894
79. G. Bottegoni, and et al. The role of fragment-based and computational methods in polypharmacology Drug Discov. Today 17 2012 23 34
80. R. Morphy, and Z. Rankovic The physicochemical challenges of designing multiple ligands J. Med. Chem. 49 2006 4961 4970
81. E. Shang, and et al. De novo design of multitarget ligands with an iterative fragment-growing strategy J. Chem. Inf. Model. 54 2014 1235 1241
82. M. Reutlinger, and et al. Multi-objective molecular de novo design by adaptive fragment prioritization Angew. Chem. Int. Ed. Engl. 53 2014 4244 4248
83. C. Lecoutey, and et al. Design of donecopride, a dual serotonin subtype 4 receptor agonist/acetylcholinesterase inhibitor with potential interest for Alzheimer's disease treatment Proc. Natl. Acad. Sci. U. S. A. 111 2014 E3825 E3830
84. L. Costantino, and D. Barlocco Designed multiple ligands: basic research vs clinical outcomes Curr. Med. Chem. 19 2012 3353 3387
85. K.T. Schomburg, and M. Rarey Benchmark data sets for structure-based computational target prediction J. Chem. Inf. Model. 54 2014 2261 2274
86. A. Koutsoukas, and et al. In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naive Bayes and Parzen-Rosenblatt window J. Chem. Inf. Model. 53 2013 1957 1966
87. M. Davies, and et al. ChEMBL web services: streamlining access to drug discovery data and utilities Nucleic Acids Res. 43 2015 W612 W620
88. P.M. Petrone, and et al. Rethinking molecular similarity: comparing compounds on the basis of biological activity ACS Chem. Biol. 7 2012 1399 1409
89. A.M. Wassermann, and et al. A screening pattern recognition method finds new and divergent targets for drugs and natural products ACS Chem. Biol. 9 2014 1622 1631
90. G. Schneider Future de novo drug design Mol. Inform. 33 2014 397 402
91. E.L. Berg Systems biology in drug discovery and development Drug Discov. Today 19 2014 113 125
92. E.E. Bolton, and et al. Integrated platform of small molecules and biological activities A.W. Ralph, C.S. David, Annual Reports in Computational Chemistry Vol. 4 2008 Elsevier 217 241
93. A.P. Bento, and et al. The ChEMBL bioactivity database: an update Nucleic Acids Res. 42 2014 D1083 D1090
94. T. Liu, and et al. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities Nucleic Acids Res. 35 2007 D198 D201
95. V. Law, and et al. DrugBank 4.0: shedding new light on drug metabolism Nucleic Acids Res. 42 2014 D1091 D1097
96. M. Kanehisa, and et al. Data, information, knowledge and principle: back to metabolism in KEGG Nucleic Acids Res. 42 2014 D199 D205
97. C. Qin, and et al. Therapeutic target database update 2014: a resource for targeted therapeutics Nucleic Acids Res. 42 2014 D1118 D1123
98. S. Gunther, and et al. SuperTarget and Matador: resources for exploring drug-target relationships Nucleic Acids Res. 36 2008 D919 D922
99. K.P. Seiler, and et al. ChemBank: a small-molecule screening and cheminformatics resource database Nucleic Acids Res. 36 2008 D351 D359
100. M. Kuhn, and et al. STITCH: 4 integration of protein-chemical interactions with user data Nucleic Acids Res. 42 2014 D401 D407
101. Z. Hu, and et al. VisANT 4.0: integrative network platform to connect genes, drugs, diseases and therapies Nucleic Acids Res. 41 2013 W225 W231
102. S. Kim Kjaerulff, and et al. ChemProt-2.0: visual navigation in a disease chemical biology database Nucleic Acids Res. 41 2013 D464 D469
103. Q.N. Hu, and et al. VNP: interactive visual network pharmacology of diseases, targets, and drugs CPT Pharmacometrics Syst. Pharmacol. 3 2014 e105
104. Z. Wang, and et al. PhIN: a protein pharmacology interaction network database CPT Pharmacometrics Syst. Pharmacol. 4 2015 e00025
105. J. von Eichborn, and et al. PROMISCUOUS: a database for network-based drug-repositioning Nucleic Acids Res. 39 2011 D1060 D1066
106. D.J. Dix, and et al. The ToxCast program for prioritizing toxicity testing of environmental chemicals Toxicol. Sci. 95 2007 5 12
107. Y. Yamanishi, and et al. DINIES: drug-target interaction network inference engine based on supervised analysis Nucleic Acids Res. 42 2014 W39 W45
108. J. Nickel, and et al. SuperPred: update on drug classification and target prediction Nucleic Acids Res. 42 2014 W26 W31
109. X. Liu, and et al. TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds Bioinformatics 31 2015 2049 2051
110. D. Carrella, and et al. Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis Bioinformatics 30 2014 1787 1788
111. D. Gfeller, and et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules Nucleic Acids Res. 42 2014 W32 W38
112. H. Luo, and et al. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome Nucleic Acids Res. 42 2014 W46 W52
113. L. Wang, and et al. TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database AAPS J. 15 2013 395 406