Pharmaceutical Research

, Volume 27, Issue 5, pp 739–749 | Cite as

In-Silico Approaches to Multi-target Drug Discovery

Computer Aided Multi-target Drug Design, Multi-target Virtual Screening
  • Xiao Hua Ma
  • Zhe Shi
  • Chunyan Tan
  • Yuyang Jiang
  • Mei Lin Go
  • Boon Chuan Low
  • Yu Zong Chen
Commentary

Abstract

Multi-target drugs against selective multiple targets improve therapeutic efficacy, safety and resistance profiles by collective regulations of a primary therapeutic target together with compensatory elements and resistance activities. Efforts have been made to employ in-silico methods for facilitating the search and design of selective multi-target agents. These methods have shown promising potential in facilitating drug discovery directed at selective multiple targets.

KEY WORDS

computer aided dug design multiple ligands multi-target multi-target drug discovery virtual screening 

References

  1. 1.
    Smalley KS, Haass NK, Brafford PA, Lioni M, Flaherty KT, Herlyn M. Multiple signaling pathways must be targeted to overcome drug resistance in cell lines derived from melanoma metastases. Mol Cancer Ther. 2006;5:1136–44.CrossRefPubMedGoogle Scholar
  2. 2.
    Pilpel Y, Sudarsanam P, Church GM. Identifying regulatory networks by combinatorial analysis of promoter elements. Nat Genet. 2001;29:153–9.CrossRefPubMedGoogle Scholar
  3. 3.
    Muller R. Crosstalk of oncogenic and prostanoid signaling pathways. J Cancer Res Clin Oncol. 2004;130:429–44.CrossRefPubMedGoogle Scholar
  4. 4.
    Sergina NV, Rausch M, Wang D, Blair J, Hann B, Shokat KM, et al. Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature. 2007;445:437–41.CrossRefPubMedGoogle Scholar
  5. 5.
    Christopher M, Overall, Kleifeld O. Validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy. Nat Rev Cancer. 2006;6:227–39.CrossRefGoogle Scholar
  6. 6.
    Force T, Krause DS, Van Etten RA. Molecular mechanisms of cardiotoxicity of tyrosine kinase inhibition. Nat Rev Cancer. 2007;7:332–44.CrossRefPubMedGoogle Scholar
  7. 7.
    Keith CT, Borisy AA, Stockwell BR. Multicomponent therapeutics for networked systems. Nat Rev Drug Discov. 2005;4:71–8.CrossRefPubMedGoogle Scholar
  8. 8.
    Larder BA, Kemp SD, Harrigan PR. Potential mechanism for sustained antiretroviral efficacy of AZT-3TC combination therapy. Science. 1995;269:696–9.CrossRefPubMedGoogle Scholar
  9. 9.
    Zhang X, Crespo A, Fernandez A. Turning promiscuous kinase inhibitors into safer drugs. Trends Biotechnol. 2008;26:295–301.CrossRefPubMedGoogle Scholar
  10. 10.
    Krug M, Hilgeroth A. Recent advances in the development of multi-kinase inhibitors. Mini Rev Med Chem. 2008;8:1312–27.CrossRefPubMedGoogle Scholar
  11. 11.
    Gill AL, Verdonk M, Boyle RG, Taylor R. A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. Curr Top Med Chem. 2007;7:1408–22.CrossRefPubMedGoogle Scholar
  12. 12.
    Nahta R, Yu D, Hung MC, Hortobagyi GN, Esteva FJ. Mechanisms of disease: understanding resistance to HER2-targeted therapy in human breast cancer. Nat Clin Pract Oncol. 2006;3:269–80.CrossRefPubMedGoogle Scholar
  13. 13.
    Tabernero J. The role of VEGF and EGFR inhibition: implications for combining anti-VEGF and anti-EGFR agents. Mol Cancer Res. 2007;5:203–20.CrossRefPubMedGoogle Scholar
  14. 14.
    Kong A, Calleja V, Leboucher P, Harris A, Parker PJ, Larijani B. HER2 oncogenic function escapes EGFR tyrosine kinase inhibitors via activation of alternative HER receptors in breast cancer cells. PLoS One. 2008;3:e2881.CrossRefPubMedGoogle Scholar
  15. 15.
    Millan MJ. Multi-target strategies for the improved treatment of depressive states: conceptual foundations and neuronal substrates, drug discovery and therapeutic application. Pharmacol Ther. 2006;110:135–370.CrossRefPubMedGoogle Scholar
  16. 16.
    Ma XH, Zheng CJ, Han LY, Xie B, Jia J, Cao ZW, et al. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives. Drug Discov Today. 2009;14:579–88.CrossRefPubMedGoogle Scholar
  17. 17.
    Jayanthi LD, Ramamoorthy S. Regulation of monoamine transporters: influence of psychostimulants and therapeutic antidepressants. Aaps J. 2005;7:E728–38.CrossRefPubMedGoogle Scholar
  18. 18.
    Kopin IJ. Monoamine oxidase and catecholamine metabolism. J Neural Transm Suppl. 1994;41:57–67.PubMedGoogle Scholar
  19. 19.
    Oechsner M, Buhmann C, Strauss J, Stuerenburg HJ. COMT-inhibition increases serum levels of dihydroxyphenylacetic acid (DOPAC) in patients with advanced Parkinson’s disease. J Neural Transm. 2002;109:69–75.CrossRefPubMedGoogle Scholar
  20. 20.
    Matzen L, van Amsterdam C, Rautenberg W, Greiner HE, Harting J, Seyfried CA, et al. 5-HT reuptake inhibitors with 5-HT (1B/1D) antagonistic activity: a new approach toward efficient antidepressants. J Med Chem. 2000;43:1149–57.CrossRefPubMedGoogle Scholar
  21. 21.
    Melloni P, Carniel G, Della Torre A, Bonsignori A, Buonamici M, Pozzi O, et al. Potential antidepressant agents, aryloxy-benzyl derivatives of ethanolamine and morpholine. Eur J Med Chem. 1984;19:235–42.Google Scholar
  22. 22.
    Jia J, Zhu F, Ma X, Cao Z, Li Y, Chen YZ. Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov. 2009;8:111–28.CrossRefPubMedGoogle Scholar
  23. 23.
    Jenwitheesuk E, Horst JA, Rivas KL, Van Voorhis WC, Samudrala R. Novel paradigms for drug discovery: computational multitarget screening. Trends Pharmacol Sci. 2008;29:62–71.CrossRefPubMedGoogle Scholar
  24. 24.
    Aluisio L, Lord B, Barbier AJ, Fraser IC, Wilson SJ, Boggs J, et al. In-vitro and in-vivo characterization of JNJ-7925476, a novel triple monoamine uptake inhibitor. Eur J Pharmacol. 2008;587:141–6.CrossRefPubMedGoogle Scholar
  25. 25.
    Maryanoff BE, McComsey DF, Gardocki JF, Shank RP, Costanzo MJ, Nortey SO, et al. Pyrroloisoquinoline antidepressants. 2. In-depth exploration of structure-activity relationships. J Med Chem. 1987;30:1433–54.CrossRefPubMedGoogle Scholar
  26. 26.
    Sathornsumetee S, Reardon DA. Targeting multiple kinases in glioblastoma multiforme. Expert Opin Investig Drugs. 2009;18:277–92.CrossRefPubMedGoogle Scholar
  27. 27.
    Meyer RD, Sacks DB, Rahimi N. IQGAP1-dependent signaling pathway regulates endothelial cell proliferation and angiogenesis. PLoS One. 2008;3:e3848.CrossRefPubMedGoogle Scholar
  28. 28.
    Ren JG, Li Z, Sacks DB. IQGAP1 modulates activation of B-Raf. Proc Natl Acad Sci U S A. 2007;104:10465–9.CrossRefPubMedGoogle Scholar
  29. 29.
    Briggs MW, Sacks DB. IQGAP proteins are integral components of cytoskeletal regulation. EMBO Rep. 2003;4:571–4.CrossRefPubMedGoogle Scholar
  30. 30.
    Li S. Src-family kinases in the development and therapy of Philadelphia chromosome-positive chronic myeloid leukemia and acute lymphoblastic leukemia. Leuk Lymphoma. 2008;49:19–26.CrossRefPubMedGoogle Scholar
  31. 31.
    Bates RC, Goldsmith JD, Bachelder RE, Brown C, Shibuya M, Oettgen P, et al. Flt-1-dependent survival characterizes the epithelial-mesenchymal transition of colonic organoids. Curr Biol. 2003;13:1721–7.CrossRefPubMedGoogle Scholar
  32. 32.
    Gockel I, Moehler M, Frerichs K, Drescher D, Trinh TT, Duenschede F, et al. Co-expression of receptor tyrosine kinases in esophageal adenocarcinoma and squamous cell cancer. Oncol Rep. 2008;20:845–50.PubMedGoogle Scholar
  33. 33.
    Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432:862–5.CrossRefPubMedGoogle Scholar
  34. 34.
    Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, et al. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J Chem Inf Model 2008;48(6)1227–37CrossRefPubMedGoogle Scholar
  35. 35.
    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.CrossRefPubMedGoogle Scholar
  36. 36.
    Deng XQ, Wang HY, Zhao YL, Xiang ML, Jiang PD, Cao ZX, et al. Pharmacophore modelling and virtual screening for identification of new Aurora-A kinase inhibitors. Chem Biol Drug Des. 2008;71:533–9.CrossRefPubMedGoogle Scholar
  37. 37.
    Deanda F, Stewart EL, Reno MJ, Drewry DH. Kinase-targeted library design through the application of the PharmPrint methodology. J Chem Inf Model. 2008;48:2395–403.CrossRefPubMedGoogle Scholar
  38. 38.
    Briem H, Gunther J. Classifying “kinase inhibitor-likeness” by using machine-learning methods. Chembiochem. 2005;6:558–66.CrossRefPubMedGoogle Scholar
  39. 39.
    Gundla R, Kazemi R, Sanam R, Muttineni R, Sarma JA, Dayam R, et al. Discovery of novel small-molecule inhibitors of human epidermal growth factor receptor-2: combined ligand and target-based approach. J Med Chem. 2008;51:3367–77.CrossRefPubMedGoogle Scholar
  40. 40.
    Clemente JC, Govindasamy L, Madabushi A, Fisher SZ, Moose RE, Yowell CA, et al. Structure of the aspartic protease plasmepsin 4 from the malarial parasite Plasmodium malariae bound to an allophenylnorstatine-based inhibitor. Acta Crystallogr D Biol Crystallogr. 2006;62:246–52.CrossRefPubMedGoogle Scholar
  41. 41.
    Wei D, Jiang X, Zhou L, Chen J, Chen Z, He C, et al. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J Med Chem. 2008;51:7882–8.CrossRefPubMedGoogle Scholar
  42. 42.
    Morphy R, Rankovic Z. The physicochemical challenges of designing multiple ligands. J Med Chem. 2006;49:4961–70.CrossRefPubMedGoogle Scholar
  43. 43.
    Morphy R. The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. J Med Chem. 2006;49:2969–78.CrossRefPubMedGoogle Scholar
  44. 44.
    Vina D, Uriarte E, Orallo F, Gonzalez-Diaz H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm. 2009;6:825–35.CrossRefPubMedGoogle Scholar
  45. 45.
    Prado-Prado FJ, Uriarte E, Borges F, Gonzalez-Diaz H. Multi-target spectral moments for QSAR and Complex Networks study of antibacterial drugs. Eur J Med Chem. 2009;44:4516–21.CrossRefPubMedGoogle Scholar
  46. 46.
    Gonzalez-Diaz H, Prado-Prado FJ. Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals. J Comput Chem. 2008;29:656–67.CrossRefPubMedGoogle Scholar
  47. 47.
    Gonzalez-Diaz 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.CrossRefPubMedGoogle Scholar
  48. 48.
    Prado-Prado FJ, Martinez de la Vega O, Uriarte E, Ubeira FM, Chou KC, Gonzalez-Diaz H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg Med Chem. 2009;17:569–75.CrossRefPubMedGoogle Scholar
  49. 49.
    Ma XH, Jia J, Zhu F, Xue Y, Li ZR, Chen YZ. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb Chem High Throughput Screen. 2009;12:344–57.CrossRefPubMedGoogle Scholar
  50. 50.
    Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, et al. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model. 2008;26:1276–86.CrossRefPubMedGoogle Scholar
  51. 51.
    Liu XH, Ma XH, Tan CY, Jiang YY, Go ML, Low BC, et al. Virtual screening of Abl inhibitors from large compound libraries by support vector machines. J Chem Inf Model. 2009;49:2101–10.CrossRefPubMedGoogle Scholar
  52. 52.
    Stommel JM, Kimmelman AC, Ying H, Nabioullin R, Ponugoti AH, Wiedemeyer R, et al. Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies. Science. 2007;318:287–90.CrossRefPubMedGoogle Scholar
  53. 53.
    Bender A, Jenkins JL, Glick M, Deng Z, Nettles JH, Davies JW. “Bayes affinity fingerprints” improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept? J Chem Inf Model. 2006;46:2445–56.CrossRefPubMedGoogle Scholar
  54. 54.
    Givehchi A, Bender A, Glen RC. Analysis of activity space by fragment fingerprints, 2D descriptors, and multitarget dependent transformation of 2D descriptors. J Chem Inf Model. 2006;46:1078–83.CrossRefPubMedGoogle Scholar
  55. 55.
    Renner S, Derksen S, Radestock S, Morchen F. Maximum common binding modes (MCBM): consensus docking scoring using multiple ligand information and interaction fingerprints. J Chem Inf Model. 2008;48:319–32.CrossRefPubMedGoogle Scholar
  56. 56.
    Erhan D, L'Heureux JP, Yue SY, Bengio Y. Collaborative filtering on a family of biological targets. J Chem Inf Model. 2006;46:626–35.CrossRefPubMedGoogle Scholar
  57. 57.
    Dragos H, Gilles M, Alexandre V. Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. J Chem Inf Model. 2009;49:1762–76.CrossRefPubMedGoogle Scholar
  58. 58.
    Guiard BP, El Mansari M, Blier P. Prospect of a dopamine contribution in the next generation of antidepressant drugs: the triple reuptake inhibitors. Curr Drug Targets. 2009;10:1069–84.Google Scholar
  59. 59.
    Wong CI, Koh TS, Soo R, Hartono S, Thng CH, McKeegan E, et al. Phase I and biomarker study of ABT-869, a multiple receptor tyrosine kinase inhibitor, in patients with refractory solid malignancies. J Clin Oncol. 2009;27:4718–26.CrossRefPubMedGoogle Scholar
  60. 60.
    Shankar DB, Li J, Tapang P, Owen McCall J, Pease LJ, Dai Y, et al. ABT-869, a multitargeted receptor tyrosine kinase inhibitor: inhibition of FLT3 phosphorylation and signaling in acute myeloid leukemia. Blood. 2007;109:3400–8.CrossRefPubMedGoogle Scholar
  61. 61.
    Guo J, Marcotte PA, McCall JO, Dai Y, Pease LJ, Michaelides MR, et al. Inhibition of phosphorylation of the colony-stimulating factor-1 receptor (c-Fms) tyrosine kinase in transfected cells by ABT-869 and other tyrosine kinase inhibitors. Mol Cancer Ther. 2006;5:1007–13.CrossRefPubMedGoogle Scholar
  62. 62.
    Sherman SI. Early clinical studies of novel therapies for thyroid cancers. Endocrinol Metab Clin North Am. 2008;37:511–24. xi.CrossRefPubMedGoogle Scholar
  63. 63.
    Lee CB, Socinski MA. Vascular endothelial growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer: a review of recent clinical trials. Rev Recent Clin Trials. 2007;2:117–20.CrossRefPubMedGoogle Scholar
  64. 64.
    Weisberg E, Roesel J, Bold G, Furet P, Jiang J, Cools J, et al. Antileukemic effects of the novel, mutant FLT3 inhibitor NVP-AST487: effects on PKC412-sensitive and -resistant FLT3-expressing cells. Blood. 2008;112:5161–70.CrossRefPubMedGoogle Scholar
  65. 65.
    Akeno-Stuart N, Croyle M, Knauf JA, Malaguarnera R, Vitagliano D, Santoro M, et al. The RET kinase inhibitor NVP-AST487 blocks growth and calcitonin gene expression through distinct mechanisms in medullary thyroid cancer cells. Cancer Res. 2007;67:6956–64.CrossRefPubMedGoogle Scholar
  66. 66.
    Trudel S, Li ZH, Wei E, Wiesmann M, Chang H, Chen C, et al. CHIR-258, a novel, multitargeted tyrosine kinase inhibitor for the potential treatment of t(4;14) multiple myeloma. Blood. 2005;105:2941–8.CrossRefPubMedGoogle Scholar
  67. 67.
    Lopes de Menezes DE, Peng J, Garrett EN, Louie SG, Lee SH, Wiesmann M, et al. CHIR-258: a potent inhibitor of FLT3 kinase in experimental tumor xenograft models of human acute myelogenous leukemia. Clin Cancer Res. 2005;11:5281–91.CrossRefPubMedGoogle Scholar
  68. 68.
    Lee SH, Lopes de Menezes D, Vora J, Harris A, Ye H, Nordahl L, et al. In vivo target modulation and biological activity of CHIR-258, a multitargeted growth factor receptor kinase inhibitor, in colon cancer models. Clin Cancer Res. 2005;11:3633–41.CrossRefPubMedGoogle Scholar
  69. 69.
    Srivastava M. Phase I trial begins for CHIR-265, a new melanoma drug. J Drugs Dermatol. 2006;5:537–537.Google Scholar
  70. 70.
    Cortes JE, Jones D, O’Brien S, Jabbour E, Ravandi F, Koller C, et al. Results of dasatinib therapy in patients with early chronic-phase chronic myeloid leukemia. J Clin Oncol. 2010;28:398–404.Google Scholar
  71. 71.
    Yu EY, Wilding G, Posadas E, Gross M, Culine S, Massard C, et al. Phase II study of dasatinib in patients with metastatic castration-resistant prostate cancer. Clin Cancer Res. 2009;15:7421–8.CrossRefPubMedGoogle Scholar
  72. 72.
    Konecny GE, Glas R, Dering J, Manivong K, Qi J, Finn RS, et al. Activity of the multikinase inhibitor dasatinib against ovarian cancer cells. Br J Cancer. 2009;101:1699–708.CrossRefPubMedGoogle Scholar
  73. 73.
    Deguchi Y, Kimura S, Ashihara E, Niwa T, Hodohara K, Fujiyama Y, et al. Comparison of imatinib, dasatinib, nilotinib and INNO-406 in imatinib-resistant cell lines. Leuk Res. 2008;32:980–3.CrossRefPubMedGoogle Scholar
  74. 74.
    Tsao AS, He D, Saigal B, Liu S, Lee JJ, Bakkannagari S, et al. Inhibition of c-Src expression and activation in malignant pleural mesothelioma tissues leads to apoptosis, cell cycle arrest, and decreased migration and invasion. Mol Cancer Ther. 2007;6:1962–72.CrossRefPubMedGoogle Scholar
  75. 75.
    Okabe S, Tauchi T, Ohyashiki K. Characteristics of dasatinib- and imatinib-resistant chronic myelogenous leukemia cells. Clin Cancer Res. 2008;14:6181–6.CrossRefPubMedGoogle Scholar
  76. 76.
    Aklilu M, Kindler HL, Donehower RC, Mani S, Vokes EE. Phase II study of flavopiridol in patients with advanced colorectal cancer. Ann Oncol. 2003;14:1270–3.CrossRefPubMedGoogle Scholar
  77. 77.
    Nitta N, Sonoda A, Seko A, Ohta S, Nagatani Y, Tsuchiya K, et al. A combination of cisplatin-eluting gelatin microspheres and flavopiridol enhances antitumour effects in a rabbit VX2 liver tumour model. Br J Radiol. 2009.Google Scholar
  78. 78.
    Christian BA, Grever MR, Byrd JC, Lin TS. Flavopiridol in chronic lymphocytic leukemia: a concise review. Clin Lymphoma Myeloma. 2009;9 Suppl 3:S179–85.CrossRefPubMedGoogle Scholar
  79. 79.
    Carvajal RD, Tse A, Shah MA, Lefkowitz RA, Gonen M, Gilman-Rosen L, et al. A phase II study of flavopiridol (Alvocidib) in combination with docetaxel in refractory, metastatic pancreatic cancer. Pancreatology. 2009;9:404–9.CrossRefPubMedGoogle Scholar
  80. 80.
    Parker BW, Kaur G, Nieves-Neira W, Taimi M, Kohlhagen G, Shimizu T, et al. Early induction of apoptosis in hematopoietic cell lines after exposure to flavopiridol. Blood. 1998;91:458–65.PubMedGoogle Scholar
  81. 81.
    Senderowicz AM. Flavopiridol: the first cyclin-dependent kinase inhibitor in human clinical trials. Invest New Drugs. 1999;17:313–20.CrossRefPubMedGoogle Scholar
  82. 82.
    Sonpavde G, Hutson TE, Sternberg CN. Pazopanib, a potent orally administered small-molecule multitargeted tyrosine kinase inhibitor for renal cell carcinoma. Expert Opin Investig Drugs. 2008;17:253–61.CrossRefPubMedGoogle Scholar
  83. 83.
    Sloan B, Scheinfeld NS. Pazopanib, a VEGF receptor tyrosine kinase inhibitor for cancer therapy. Curr Opin Investig Drugs. 2008;9:1324–35.PubMedGoogle Scholar
  84. 84.
    Kumar R, Knick VB, Rudolph SK, Johnson JH, Crosby RM, Crouthamel MC, et al. Pharmacokinetic-pharmacodynamic correlation from mouse to human with pazopanib, a multikinase angiogenesis inhibitor with potent antitumor and antiangiogenic activity. Mol Cancer Ther. 2007;6:2012–21.CrossRefPubMedGoogle Scholar
  85. 85.
    Kantarjian H, Sawyers C, Hochhaus A, Guilhot F, Schiffer C, Gambacorti-Passerini C, et al. Hematologic and cytogenetic responses to imatinib mesylate in chronic myelogenous leukemia. N Engl J Med. 2002;346:645–52.CrossRefPubMedGoogle Scholar
  86. 86.
    Lopes LF, Bacchi CE. Imatinib treatment for gastrointestinal stromal tumor (GIST). J Cell Mol Med. 2009 doi:10.1111/j.1582-4934.2009.00983.
  87. 87.
    Radujkovic A, Schad M, Topaly J, Veldwijk MR, Laufs S, Schultheis BS, et al. Synergistic activity of imatinib and 17-AAG in imatinib-resistant CML cells overexpressing BCR-ABL–Inhibition of P-glycoprotein function by 17-AAG. Leukemia. 2005;19:1198–206.CrossRefPubMedGoogle Scholar
  88. 88.
    Emanuel S, Rugg CA, Gruninger RH, Lin R, Fuentes-Pesquera A, Connolly PJ, et al. The in vitro and in vivo effects of JNJ-7706621: a dual inhibitor of cyclin-dependent kinases and aurora kinases. Cancer Res. 2005;65:9038–46.CrossRefPubMedGoogle Scholar
  89. 89.
    Schwartz J. Current combination chemotherapy regimens for metastatic breast cancer. Am J Health Syst Pharm. 2009;66:S3–8.CrossRefPubMedGoogle Scholar
  90. 90.
    McHugh LA, Kriajevska M, Mellon JK, Griffiths TR. Combined treatment of bladder cancer cell lines with lapatinib and varying chemotherapy regimens—evidence of schedule-dependent synergy. Urology. 2007;69:390–4.CrossRefPubMedGoogle Scholar
  91. 91.
    DeAngelo DJ, Stone RM, Heaney ML, Nimer SD, Paquette RL, Klisovic RB, et al. Phase 1 clinical results with tandutinib (MLN518), a novel FLT3 antagonist, in patients with acute myelogenous leukemia or high-risk myelodysplastic syndrome: safety, pharmacokinetics, and pharmacodynamics. Blood. 2006;108:3674–81.CrossRefPubMedGoogle Scholar
  92. 92.
    Corbin AS, Griswold IJ, La Rosee P, Yee KW, Heinrich MC, Reimer CL, et al. Sensitivity of oncogenic KIT mutants to the kinase inhibitors MLN518 and PD180970. Blood. 2004;104:3754–7.CrossRefPubMedGoogle Scholar
  93. 93.
    Mohapatra S, Coppola D, Riker AI, Pledger WJ. Roscovitine inhibits differentiation and invasion in a three-dimensional skin reconstruction model of metastatic melanoma. Mol Cancer Res. 2007;5:145–51.CrossRefPubMedGoogle Scholar
  94. 94.
    Gusani NJ, Jiang Y, Kimchi ET, Staveley-O’Carroll KF, Cheng H, Ajani JA. New pharmacological developments in the treatment of hepatocellular cancer. Drugs. 2009;69:2533–40.CrossRefPubMedGoogle Scholar
  95. 95.
    Kim S, Yazici YD, Calzada G, Wang ZY, Younes MN, Jasser SA, et al. Sorafenib inhibits the angiogenesis and growth of orthotopic anaplastic thyroid carcinoma xenografts in nude mice. Mol Cancer Ther. 2007;6:1785–92.CrossRefPubMedGoogle Scholar
  96. 96.
    Demetri GD, van Oosterom AT, Garrett CR, Blackstein ME, Shah MH, Verweij J, et al. Efficacy and safety of sunitinib in patients with advanced gastrointestinal stromal tumour after failure of imatinib: a randomised controlled trial. Lancet. 2006;368:1329–38.CrossRefPubMedGoogle Scholar
  97. 97.
    Motzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Rixe O, et al. Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med. 2007;356:115–24.CrossRefPubMedGoogle Scholar
  98. 98.
    Bates D. ZD-6474. AstraZeneca. Curr Opin Investig Drugs. 2003;4:1468–72.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Xiao Hua Ma
    • 1
    • 3
  • Zhe Shi
    • 1
  • Chunyan Tan
    • 2
  • Yuyang Jiang
    • 2
  • Mei Lin Go
    • 1
  • Boon Chuan Low
    • 3
  • Yu Zong Chen
    • 1
  1. 1.Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and EngineeringNational University of SingaporeSingaporeSingapore
  2. 2.The Key Laboratory of Chemical Biology, Guangdong Province, The Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China
  3. 3.Department of Biological ScienceNational University of SingaporeSingaporeSingapore

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