Drugs Polypharmacology by in Silico Methods: New Opportunities in Drug Discovery

Current Pharmaceutical Design

Volumn 22, Issue 21, 2016, Pages 3073-3081

  Lauria, Antonino ^a   Bonsignore, Riccardo ^a   Bartolotta, Roberta ^a   Perricone, Ugo ^a   Martorana, Annamaria ^a   Gentile, Carla ^a  

^a UNIVERSITY OF PALERMO   (Italy)

Author keywords

Biological and chemical databases; Computational methods; Drugs; Multitarget activity; Polypharmacology; Repurposing

Indexed keywords

ARTICLE; BINDING AFFINITY; CHEMICAL DATABASE; COMPUTER MODEL; DRUG DESIGN; DRUG DEVELOPMENT; DRUG REPOSITIONING; DRUG SELECTIVITY; GENE EXPRESSION; HUMAN; MEDICATION COMPLIANCE; MOLECULAR BIOLOGY; MOLECULAR DOCKING; MOLECULAR LIBRARY; PHYSICAL CHEMISTRY; POLYPHARMACOLOGY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN DATA BANK; PROTEIN STRUCTURE; PROTEOCHEMOMETRIC MODELLING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; SUPPORT VECTOR MACHINE; CHEMICAL STRUCTURE; COMPUTER SIMULATION; SOFTWARE;

COMPUTER SIMULATION; DRUG DISCOVERY; HUMANS; MOLECULAR STRUCTURE; POLYPHARMACOLOGY; SOFTWARE;

EID: 84975746739     PISSN: 13816128     EISSN: 18734286     Source Type: Journal    
DOI: 10.2174/1381612822666160224142323     Document Type: Article

References (111)

1. Domino EF. Chloral hydrate potentiates anticoagulants. JAMA 1971; 215: 1160.
2. Kenny B, Ballard S, Blagg J, Fox D. Pharmacological options in the treatment of benign prostatic hyperplasia. J Med Chem 1997; 40: 1293-315.
3. Calhoun DA. Use of single-pill combination therapy in the evolving paradigm of hypertension management. Expert Opin Pharmacother 2009; 10: 1869-74.
4. Eastman RT, Fidock DA. Artemisinin-based combination therapies: a vital tool in efforts to eliminate malaria. Nat Rev Microbiol 2009; 7: 864-74.
5. Conway D, Cohen JA. Combination therapy in multiple sclerosis. Lancet Neurol 2010; 9: 299-308.
6. Boran ADW, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr. Opin. Drug Discov Dev 2010; 13: 297-309.
7. Peters J-U. Polypharmacology-foe or friend? J Med Chem 2013; 56: 8955-71.
8. Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 2014; 57: 7874-87.
9. Azmi AS. Network pharmacology for cancer drug discovery: are we there yet? Future Med Chem 2012; 4: 939-41.
10. Faivre S, Demetri G, Sargent W, Raymond E. Molecular basis for sunitinib efficacy and future clinical development. Nat Rev Drug Discov 2007; 6: 734-45.
11. Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome through polypharmacology. Nat Rev Cancer 2010; 10: 130-7.
12. Youdim MBH, Buccafusco JJ. Multi-functional drugs for various CNS targets in the treatment of neurodegenerative disorders. Trends Pharmacol Sci 2005; 26: 27-35.
13. Miyamoto S, Miyake N, Jarskog LF, Fleischhacker WW, Lieberman JA. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry 2012; 17: 1206-27.
14. Emsley R. Drugs in development for the treatment of schizophrenia. Expert Opin Investig Drugs 2009; 18: 1103-18.
15. Connolly HM, Crary JL, McGoon MD, et al. Valvular heart disease associated with fenfluramine-phentermine. N Engl J Med 1997; 337: 581-8.
16. Mestres J, Gregori-Puigjané E, Valverde S, Solé RV. Data completeness--the Achilles heel of drug-target networks. Nat Biotechnol 2008; 26: 983-4.
17. Wishart DS, Knox C, Guo AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 2009; 37: D603-10.
18. Garcia-Serna R, Mestres J. Chemical probes for biological systems. Drug Discov Today 2011; 16: 99-106.
19. WHO. Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment. World Health Organization 2014.
20. Jalencas X, Mestres J. On the origins of drug polypharmacology. MedChemComm 2012; 4: 80-7.
21. Hu Y, Bajorath J. Compound promiscuity: what can we learn from current data? Drug Discov Today 2013; 18: 644-50.
22. Chong CR, Sullivan DJ. New uses for old drugs. Nature 2007; 448: 645-6.
23. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008; 4: 682-90.
24. Reddy AS, Zhang S. Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 2013; 6.
25. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinforma Oxf Engl 2008; 24: i232-240.
26. Dar AC, Das TK, Shokat KM, Cagan RL. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 2012; 486: 80-4.
27. Bender A, Young D, Jenkins J, et al. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprints. Comb Chem High Throughput Screen 2007; 10: 719-31.
28. Jenkins JL, Bender A, Davies JW. In silico target fishing: Predicting biological targets from chemical structure. Drug Discov Today Technol 2006; 3: 413-21.
29. Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 2014; 54: 1676-86.
30. González-Díaz H, Prado-Prado FJ, Santana L, Uriarte E. Unify QSAR approach to antimicrobials. Part 1: Predicting antifungal activity against different species. Bioorg Med Chem 2006; 14: 5973-80.
31. González-DÍaz H, Prado-Prado FJ. Unified QSAR and networkbased computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J Comput Chem 2008; 29: 656-67.
32. Prado-Prado FJ, Uriarte E, Borges F, González-Díaz H. Multitarget spectral moments for QSAR and Complex Networks study of antibacterial drugs. Eur J Med Chem 2009; 44: 4516-21.
33. Marzaro G, Chilin A, Guiotto A, et al. Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 2011; 46: 2185-92.
34. García I, Fall Y, Gómez G. Using topological indices to predict anti-alzheimer and anti-parasitic GSK-3 inhibitors by multi-target QSAR in silico screening. Molecules 2010; 15: 5408-22.
35. García I, Fall Y, Gómez G, González-Díaz H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers 2011; 15: 561-7.
36. Liu Q, Zhou H, Liu L, Chen X, Zhu R, Cao Z. Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study. BMC Bioinformatics 2011; 12: 294.
37. Rollinger JM. Accessing target information by virtual parallel screening—The impact on natural product research. PhytoChem Lett 2009; 2: 53-8.
38. Wolber G, Dornhofer AA, Langer T. Efficient overlay of small organic molecules using 3D pharmacophores. J Comput Aided Mol Des 2007; 20(12): 773-88.
39. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 2005; 45: 160-9.
40. Sayle RA. So you think you understand tautomerism? J Comput Aided Mol Des 2010; 24: 485-96.
41. Liu X, Ouyang S, Yu B, et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 2010; 38: W609-614.
42. Adams JC, Keiser MJ, Basuino L, et al. A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Comput Biol 2009; 5: e1000474.
43. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Pujadas G, Garcia-Vallve S. Tools for in silico target fishing. Methods 2015; 71: 98-103.
44. Maggiora G, Vogt M, Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem 2014; 57: 3186-204.
45. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007; 25: 197-206.
46. Pérez-Nueno VI, Karaboga AS, Souchet M, Ritchie DW. GES polypharmacology fingerprints: a novel approach for drug repositioning. J Chem Inf Model 2014; 54: 720-34.
47. Johnson MA, Maggiora GM, Meeting ACS. Concepts and applications of molecular similarity. USA: Wiley 1990.
48. Chen B, McConnell KJ, Wale N, Wild DJ, Gifford EM. Comparing bioassay response and similarity ensemble approaches to probing protein pharmacology. Bioinformatics 2011; 27: 3044-9.
49. Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 2014; 42: W32-W38.
50. Lauria A, Tutone M, Almerico AM. Virtual lock-and-key approach: The in silico revival of Fischer model by means of molecular descriptors. Eur J Med Chem 2011; 46: 4274-80.
51. Scotti L, Mendonça Júnior FJB, Ishiki H, et al. Docking studies for multi-target drugs. Curr Drug Targets 2015; [Epub ahead of print].
52. Hu B, Lill MA. PharmDock: a pharmacophore-based docking program. J Cheminformatics 2014; 6: 14.
53. Wang YC, Zhang CH, Deng NY, Wang Y. Kernel-based data fusion improves the drug-protein interaction prediction. Comput Biol Chem 2011; 35: 353-62.
54. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 2015; gkv315.
55. Wei D, Jiang X, Zhou L, et al. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J Med Chem 2008; 51: 7882-8.
56. Gozalbes R, Simon L, Froloff N, Sartori E, Monteils C, Baudelle R. Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries. J Med Chem 2008; 51: 3124-32.
57. Ehrman TM, Barlow DJ, Hylands PJ. In silico search for multitarget anti-inflammatories in Chinese herbs and formulas. Bioorg Med Chem 2010; 18: 2204-18.
58. Luo H, Chen J, Shi L, et al. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Res 2011; 39: W492-8.
59. Cortés-Ciriano I, Ain QU, Subramanian V, et al. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. MedChemComm 2015; 6: 24-50.
60. Paricharak S, Cortés-Ciriano I, IJzerman AP, Malliavin TE, Bender A. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. J Cheminformatics 2015; 7: 15.
61. Lauria A, Tutone M, Barone G, Almerico AM. Multivariate analysis in the identification of biological targets for designed molecular structures: The BIOTA protocol. Eur J Med Chem 2014; 75: 106-10.
62. SCOPUS http: //www.scopus.com (Accessed October 20 2015).
63. Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: Current databases, methods and applications. J Proteomics 2011; 74: 2554-74.
64. Bender A, Scheiber J, Glick M, et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2007; 2: 861-73.
65. Nidhi, Glick M, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases. J Chem Inf Model 2006; 46: 1124-33.
66. Poroikov V, Filimonov D, Lagunin A, Gloriozova T, Zakharov A. PASS: identification of probable targets and mechanisms of toxicity. SAR QSAR Environ Res 2007; 18: 101-10.
67. Al-Ali H, Lee D-H, Danzi MC, et al. Rational polypharmacology: systematically identifying and engaging multiple drug targets to promote axon growth. ACS Chem Biol 2015; 10: 1939-51.
68. Shi Z, Ma XH, Qin C, et al. Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries. J Mol Graph Model 2012; 32: 49-66.
69. Ma XH, Wang R, Tan CY, et al. Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines. Mol Pharm 2010; 7: 1545-60.
70. Li Y, Tan C, Gao C, et al. Discovery of benzimidazole derivatives as novel multi-target EGFR, VEGFR-2 and PDGFR kinase inhibitors. Bioorg Med Chem 2011; 19: 4529-35.
71. Zhang C, Tan C, Zu X, et al. Exploration of (S)-3-aminopyrrolidine as a potentially interesting scaffold for discovery of novel Abl and PI3K dual inhibitors. Eur J Med Chem 2011; 46: 1404-14.
72. Luan X, Gao C, Zhang N, et al. Exploration of acridine scaffold as a potentially interesting scaffold for discovering novel multi-target VEGFR-2 and Src kinase inhibitors. Bioorg Med Chem 2011; 19: 3312-9.
73. Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 2010; 107: 14621-6.
74. Emig D, Ivliev A, Pustovalova O, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS One 2013; 8: e60618.
75. Filimonov DA, Poro_kov VV, Karaicheva EI, et al. The computerized prediction of the spectrum of biological activity of chemical compounds by their structural formula: the PASS system. Prediction of Activity Spectra for Substance. Eksp Klin Farmakol 1995; 58: 56-62.
76. Kauvar LM, Higgins DL, Villar HO, et al. Predicting ligand binding to proteins by affinity fingerprinting. Chem Biol 1995; 2: 107-18.
77. Haggarty SJ, Clemons PA, Schreiber SL. Chemical genomic profiling of biological networks using graph theory and combinations of small molecule perturbations. J Am Chem Soc 2003; 125: 10543-5.
78. Kim Y, Arai MA, Arai T, et al. Relationship of stereochemical and skeletal diversity of small molecules to cellular measurement space. J Am Chem Soc 2004; 126: 14740-5.
79. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Biospectra analysis: model proteome characterizations for linking molecular structure and biological response. J Med Chem 2005; 48: 6918-25.
80. Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006; 313: 1929-35.
81. Tanaka HT, Ikeda M, Chiaki H. Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition. Proc Int Conf Automat Face Gesture Recogn 1998; 372-7.
82. Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat Biotechnol 2006; 24: 805-15.
83. Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011; 12: 303-11.
84. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. BindingDB: a web-accessible database of experimentally determined proteinligand binding affinities. Nucleic Acids Res 2007; 35: D198-201.
85. Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012; 40: D1100-7.
86. Bryant S. PubChem: an information resource linking chemistry and biology. Abstr Pap Am Chem S 2007; 231.
87. Bolton EE, Wang Y, Thiessen PA, Bryant SH. Chapter 12-Pub-Chem: Integrated Platform of Small Molecules and Biological Activities. Annu Rep Comput Chem 2008; 4: 217-41.
88. Björling E, Uhlén M. Antibodypedia, a portal for sharing antibody and antigen validation data. Mol Cell Proteomics 2008; 7: 2028-37.
89. Cottingham K. Antibodypedia seeks to answer the question: “How good is that antibody?” J Proteome Res 2008; 7: 4213.
90. Kiermer V. Antibodypedia. Nat Methods 2008; 5: 860.
91. Chambers J, Davies M, Gaulton A, et al. UniChem: a unified chemical structure cross-referencing and identifier tracking system. J Cheminformatics 2013; 5: 3.
92. Chambers J, Davies M, Gaulton A, Papadatos G, Hersey A, Overington JP. UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers. J Cheminformatics 2014; 6: 43.
93. ChEMBL. www.ebi.ac.uk/chembl/ (Accessed June 03 2015).
94. Wang Y, Xiao J, Suzek TO, et al. PubChem’s BioAssay Database. Nucleic Acids Res 2012; 40: D400-12.
95. PubChem. http: //pubchem.ncbi.nlm.nih.gov/ (Accessed June 03 2015).
96. Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res 2011; 39: D1035-1041.
97. IUPHAR BPS. www.guidetopharmacology.org (Accessed June 03 2015).
98. Alexander SPH, Mathie A, Peters JA. Guide to Receptors and Channels (GRAC). 5th edition. Br J Pharmacol 2011; 164 (Suppl 1): S1-324.
99. Sharman JL, Mpamhanga CP, Spedding M, et al. IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data. Nucleic Acids Res 2011; 39: D534-8.
100. Harmar AJ, Hills RA, Rosser EM, et al. IUPHAR-DB: the IUPHAR database of G protein-coupled receptors and ion channels. Nucleic Acids Res 2009; 37: D680-5.
101. Qin C, Zhang C, Zhu F, et al. Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res 2014; 42: D1118-1123.
102. Nair R, Liu J, Soong T-T, et al. Structural genomics is the largest contributor of novel structural leverage. J Struct Funct Genom 2009; 10: 181-91.
103. Terwilliger TC, Bricogne G. Continuous mutual improvement of macromolecular structure models in the PDB and of X-ray crystallographic software: the dual role of deposited experimental data. Acta Crystallogr. D Biol Crystallogr 2014; 70: 2533-43.
104. Brown EN, Ramaswamy S. Quality of protein crystal structures. Acta Crystallogr D Biol Crystallogr 2007; 63: 941-50.
105. Wang R, Fang X, Lu Y, Wang S. The PDBbind database: collection of binding affinities for protein_ligand complexes with known three-dimensional structures. J Med Chem 2004; 47: 2977-80.
106. Li Y, Liu Z, Li J, et al. Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set. J Chem Inf Model 2014; 54: 1700-16.
107. Li Y, Han L, Liu Z, Wang R. Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 2014; 54: 1717-36.
108. Paul N, Kellenberger E, Bret G, Müller P, Rognan D. Recovering the true targets of specific ligands by virtual screening of the protein data bank. Prot Struct Funct Bioinform 2004; 54: 671-80.
109. Marcou G, Rognan D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 2007; 47: 195-207.
110. Meslamani J, Rognan D, Kellenberger E. sc-PDB: a database for identifying variations and multiplicity of “druggable” binding sites in proteins. Bioinformatics 2011; 27: 1324-6.
111. Desaphy J, Bret G, Rognan D, Kellenberger E. sc-PDB: a 3Ddatabase of ligandable binding sites—10 years on. Nucleic Acids Res 2015; 43: D399-404.