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Expert Review of Clinical Pharmacology Volume 6, 2013 - Issue 1
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Review

Polypharmacology: drug discovery for the future

A Srinivas Reddy Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, MD Anderson Cancer Center, Houston, TX, USA
&
Shuxing Zhang Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, MD Anderson Cancer Center, Houston, TX, USA. [email protected]
Pages 41-47 | Published online: 10 Jan 2014

References

  • Dimasi JA. Risks in new drug development: approval success rates for investigational drugs. Clin. Pharmacol. Ther. 69(5), 297–307 (2001).
  • Dickson M, Gagnon JP. Key factors in the rising cost of new drug discovery and development. Nat. Rev. Drug Discov. 3(5), 417–429 (2004).
  • Hopkins AL, Groom CR. The druggable genome. Nat. Rev. Drug Discov. 1(9), 727–730 (2002).
  • Zambrowicz BP, Sands AT. Knockouts model the 100 best-selling drugs–will they model the next 100? Nat. Rev. Drug Discov. 2(1), 38–51 (2003).
  • Yook SH, Oltvai ZN, Barabási AL. Functional and topological characterization of protein interaction networks. Proteomics 4(4), 928–942 (2004).
  • Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature 432(7019), 855–861 (2004).
  • Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4(11), 682–690 (2008).
  • Hopkins AL. Drug discovery: predicting promiscuity. Nature 462(7270), 167–168 (2009).
  • Apsel B, Blair JA, Gonzalez B et al. Targeted polypharmacology: discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nat. Chem. Biol. 4(11), 691–699 (2008).
  • Simon Z, Peragovics A, Vigh-Smeller M et al. Drug effect prediction by polypharmacology-based interaction profiling. J. Chem. Inf. Model. 52(1), 134–145 (2012).
  • Briansó F, Carrascosa MC, Oprea TI, Mestres J. Cross-pharmacology analysis of G protein-coupled receptors. Curr. Top. Med. Chem. 11(15), 1956–1963 (2011).
  • Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat. Biotechnol. 24(7), 805–815 (2006).
  • Yildirim MA, Goh KI, Cusick ME, Barabási AL, Vidal M. Drug-target network. Nat. Biotechnol. 25(10), 1119–1126 (2007).
  • Durrant JD, Amaro RE, Xie L et al. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput. Biol. 6(1), e1000648 (2010).
  • Oprea TI, Mestres J. Drug repurposing: far beyond new targets for old drugs. AAPS J. 14(4), 759–763 (2012).
  • Boran ADW, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr. Opin. Drug Discov. Develop. 13(3), 297–309 (2010).
  • Boran ADW, Iyengar R. Systems pharmacology. Mount Sinai J. Med. 77(4), 333–344 (2010).
  • Xie L, Xie L, Kinnings SL, Bourne PE. Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu. Rev. Pharmacol. Toxicol. 52, 361–379 (2012).
  • Hopkins AL. Network pharmacology. Nat. Biotechnol. 25(10), 1110–1111 (2007).
  • Oprea TI, Nielsen SK, Ursu O et al. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing. Mol. Inform. 30(2–3), 100–111 (2011).
  • Achenbach J, Tiikkainen P, Franke L, Proschak E. Computational tools for polypharmacology and repurposing. Future Med. Chem. 3(8), 961–968 (2011).
  • Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232–i240 (2008).
  • Dar AC, Das TK, Shokat KM, Cagan RL. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486(7401), 80–84 (2012).
  • Knox C, Law V, Jewison T et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39(Database issue), D1035–D1041 (2011).
  • Simpson MR, Simpson NR, Masheter HC. New drugs. 8. Flufenamic acid in rheumatoid arthritis. Comparison with aspirin and the results of extended treatment. Ann. Phys. Med. 8(6), 208–213 (1966).
  • Berman J, Haffajee CI, Alpert JS. Therapy of symptomatic pericarditis after myocardial infarction: retrospective and prospective studies of aspirin, indomethacin, prednisone, and spontaneous resolution. Am. Heart J. 101(6), 750–753 (1981).
  • Durongpisitkul K, Gururaj VJ, Park JM, Martin CF. The prevention of coronary artery aneurysm in Kawasaki disease: a meta-analysis on the efficacy of aspirin and immunoglobulin treatment. Pediatrics 96(6), 1057–1061 (1995).
  • Grundmann K, Jaschonek K, Kleine B, Dichgans J, Topka H. Aspirin non-responder status in patients with recurrent cerebral ischemic attacks. J. Neurol. 250(1), 63–66 (2003).
  • Amory JK, Amory DW. Dosing frequency of aspirin and prevention of heart attacks and strokes. Am. J. Med. 120(4), e5; author reply e7 (2007).
  • Daya S. Recurrent spontaneous early pregnancy loss and low dose aspirin. Minerva Ginecol. 55(5), 441–449 (2003).
  • Baron JA. Aspirin and cancer: trials and observational studies. J. Natl Cancer Inst. 104(16), 1199–1200 (2012).
  • DeBusk RF, Pepine CJ, Glasser DB, Shpilsky A, DeRiesthal H, Sweeney M. Efficacy and safety of sildenafil citrate in men with erectile dysfunction and stable coronary artery disease. Am. J. Cardiol. 93(2), 147–153 (2004).
  • Barf T, Kaptein A. Irreversible protein kinase inhibitors: balancing the benefits and risks. J. Med. Chem. 55(14), 6243–6262 (2012).
  • Baumann KH, du Bois A, Meier W et al. A phase II trial (AGO 2.11) in platinum-resistant ovarian cancer: a randomized multicenter trial with sunitinib (SU11248) to evaluate dosage, schedule, tolerability, toxicity and effectiveness of a multitargeted receptor tyrosine kinase inhibitor monotherapy. Ann. Oncol. 23(9), 2265–2271 (2012).
  • Winum JY, Maresca A, Carta F, Scozzafava A, Supuran CT. Polypharmacology of sulfonamides: pazopanib, a multitargeted receptor tyrosine kinase inhibitor in clinical use, potently inhibits several mammalian carbonic anhydrases. Chem. Commun. 48(66), 8177–8179 (2012).
  • Ulahannan SV, Brahmer JR. Antiangiogenic agents in combination with chemotherapy in patients with advanced non-small cell lung cancer. Cancer Invest. 29(4), 325–337 (2011).
  • Konopa K, Jassem J. The role of pemetrexed combined with targeted agents for non-small cell lung cancer. Curr. Drug Targets 11(1), 2–11 (2010).
  • Keedy VL, Sandler AB. Inhibition of angiogenesis in the treatment of non-small cell lung cancer. Cancer Sci. 98(12), 1825–1830 (2007).
  • Schmid A, Blank LM. Systems biology: hypothesis-driven omics integration. Nat. Chem. Biol. 6(7), 485–487 (2010).
  • Joyce AR, Palsson BØ. The model organism as a system: integrating ‘omics’ data sets. Nat. Rev. Mol. Cell Biol. 7(3), 198–210 (2006).
  • Zhao S, Iyengar R. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol. 52, 505–521 (2012).
  • Irwin JJ, Shoichet BK. ZINC – a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45(1), 177–182 (2005).
  • Wheeler DL, Barrett T, Benson DA et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 36(Database issue), D13–D21 (2008).
  • Feng Z, Chen L, Maddula H et al. Ligand Depot: a data warehouse for ligands bound to macromolecules. Bioinformatics 20(13), 2153–2155 (2004).
  • Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40(Database issue), D109–D114 (2012).
  • Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197–206 (2007).
  • Lounkine E, Keiser MJ, Whitebread S et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403), 361–367 (2012).
  • Barabási AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004).
  • Zhang AH, Sun H, Yang B, Wang XJ. Predicting new molecular targets for rhein using network pharmacology. BMC Systems Biol. 6, 20 (2012).
  • Cheng F, Zhou Y, Li W, Liu G, Tang Y. Prediction of chemical-protein interactions network with weighted network-based inference method. PLoS ONE 7(7), e41064 (2012).
  • Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008).
  • Davis AP, King BL, Mockus S et al. The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res. 39(Database issue), D1067–D1072 (2011).
  • Grinter SZ, Liang Y, Huang SY, Hyder SM, Zou X. An inverse docking approach for identifying new potential anti-cancer targets. J. Mol. Graph. Model. 29(6), 795–799 (2011).
  • Hui-fang L, Qing S, Jian Z, Wei F. Evaluation of various inverse docking schemes in multiple targets identification. J. Mol. Graph. Model. 29(3), 326–330 (2010).
  • Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43(2), 217–226 (2001).
  • Abdulhameed MDM, Chaudhury S, Singh N, Sun H, Wallqvist A, Tawa GJ. Exploring polypharmacology using a ROCS-based target fishing approach. J. Chem. Inf. Model. 52(2), 492–505 (2012).
  • Tian L, Zhang S. Mapping drug-target interaction networks. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2336–2339 (2009).
  • Adams JC, Keiser MJ, Basuino L et al. A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Comput. Biol. 5(8), e1000474 (2009).
  • Morrow JK, Tian L, Zhang S. Molecular networks in drug discovery. Crit. Rev. Biomed. Eng. 38(2), 143–156 (2010).
  • Berman HM, Westbrook J, Feng Z et al. The Protein Data Bank. Nucleic Acids Res. 28(1), 235–242 (2000).
  • Csizmadia F. JChem: Java applets and modules supporting chemical database handling from web browsers. J. Chem. Inf. Comput. Sci. 40(2), 323–324 (2000).
  • Wren JD, Bekeredjian R, Stewart JA, Shohet RV, Garner HR. Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 20(3), 389–398 (2004).
  • Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 14(1), 133–141 (2012).
  • Wiegers TC, Davis AP, Cohen KB, Hirschman L, Mattingly CJ. Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD). BMC Bioinformatics 10, 326 (2009).
  • 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. 46(3), 1124–1133 (2006).
  • Kuhn M, von Mering C, Campillos M, Jensen LJ, Bork P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 36(Database issue), D684–D688 (2008).
  • Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35(Database issue), D198–D201 (2007).
  • Olah M, Mracec M, Ostopovici L et al. WOMBAT: world of molecular bioactivity. In: Chemoinformatics in Drug Discovery. Oprea TI (Ed.). Wiley-VCH, NY, USA, 223–239 (2004).
  • Benson ML, Smith RD, Khazanov NA et al. Binding MOAD, a high-quality protein-ligand database. Nucleic Acids Res. 36(Database issue), D674–D678 (2008).
  • Ravot E, Lisziewicz J, Lori F. New uses for old drugs in HIV infection: the role of hydroxyurea, cyclosporin and thalidomide. Drugs 58(6), 953–963 (1999).
  • ChongCR, SullivanDJ Jr. New uses for old drugs. Nature 448(7154), 645–646 (2007).
  • Chen D, Dou QP. New uses for old copper-binding drugs: converting the pro-angiogenic copper to a specific cancer cell death inducer. Expert Opin. Ther. Targets 12(6), 739–748 (2008).
  • Sannella AR, Casini A, Gabbiani C et al. New uses for old drugs. Auranofin, a clinically established antiarthritic metallodrug, exhibits potent antimalarial effects in vitro: mechanistic and pharmacological implications. FEBS Lett. 582(6), 844–847 (2008).
  • Vazquez-Martin A, Lopez-Bonetc E, Cufi S et al. Repositioning chloroquine and metformin to eliminate cancer stem cell traits in pre-malignant lesions. Drug Resist. Updat. 14(4–5), 212–223 (2010).
  • Allison M. NCATS launches drug repurposing program. Nat. Biotechnol. 30(7), 571–572 (2012).
  • Hecker N, Ahmed J, von Eichborn J et al. SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res. 40(Database issue), D1113–D1117 (2012).
  • Sharman JL, Mpamhanga CP, Spedding M et al.; NC-IUPHAR. IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data. Nucleic Acids Res. 39(Database issue), D534–D538 (2011).
  • Wang Y, Xiao J, Suzek TO et al. PubChem’s BioAssay Database. Nucleic Acids Res. 40(Database issue), D400–D412 (2012).
  • Gaulton A, Bellis LJ, Bento AP et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40(Database issue), D1100–D1107 (2012).
  • Davis AP, Murphy CG, Rosenstein MC, Wiegers TC, Mattingly CJ. The Comparative Toxicogenomics Database facilitates identification and understanding of chemical-gene-disease associations: arsenic as a case study. BMC Med. Genomics 1, 48 (2008).
  • Hirschman L, Burns GA, Krallinger M et al. Text mining for the biocuration workflow. Database (Oxford) 2012, bas020 (2012).
  • Krallinger M, Leitner F, Vazquez M et al. How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience. Database (Oxford) 2012, bas017 (2012).
  • Dowell KG, McAndrews-Hill MS, Hill DP, Drabkin HJ, Blake JA. Integrating text mining into the MGI biocuration workflow. Database (Oxford) 2009, bap019 (2009).
  • Kolb P, Ferreira RS, Irwin JJ, Shoichet BK. Docking and chemoinformatic screens for new ligands and targets. Curr. Opin. Biotechnol. 20(4), 429–436 (2009).

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