Functional pathway pharmacology: Chemical tools, pathway knowledge and mechanistic model-based interpretation of experimental data

Current Opinion in Chemical Biology

Volumn 14, Issue 4, 2010, Pages 489-497

  Hendriks, Bart S ^a  

^a MERRIMACK PHARMACEUTICALS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PHOSPHOTRANSFERASE; PROTEIN TYROSINE KINASE INHIBITOR;

BINDING AFFINITY; BIOLOGICAL ACTIVITY; COMPARATIVE STUDY; DATA ANALYSIS; EXPERIMENTAL MODEL; EXPERIMENTAL STUDY; HUMAN; KINETICS; KNOWLEDGE; MASS SPECTROMETRY; PROTEIN INTERACTION; QUANTITATIVE ANALYSIS; REVIEW; SIGNAL TRANSDUCTION;

COMPUTATIONAL BIOLOGY; DRUG DELIVERY SYSTEMS; DRUG DISCOVERY; PHARMACEUTICAL PREPARATIONS; SIGNAL TRANSDUCTION; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77955337500     PISSN: 13675931     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cbpa.2010.06.167     Document Type: Review

References (59)

1. Rinehart J., Adjei A.A., Lorusso P.M., Waterhouse D., Hecht J.R., Natale R.B., Hamid O., Varterasian M., Asbury P., Kaldjian E.P., et al. Multicenter phase II study of the oral MEK inhibitor, CI-1040, in patients with advanced non-small-cell lung, breast, colon, and pancreatic cancer. J Clin Oncol 2004, 22:4456-4462.
2. on World Wide Web. http://www.clinicaltrials.gov/ct2/show/NCT00174369%3Fterm=pd-325901%26rank=1.
3. Alten R.E., Zerbini C., Jeka S., Irazoque F., Khatib F., Emery P., Bertasso A., Rabbia M., Caulfield J.P. Efficacy and safety of pamapimod in patients with active rheumatoid arthritis receiving stable methotrexate therapy. Ann Rheum Dis 2010 Feb, 69(2):364-367.
4. Cohen S.B., Cheng T.T., Chindalore V., Damjanov N., Burgos-Vargas R., Delora P., Zimany K., Travers H., Caulfield J.P. Evaluation of the efficacy and safety of pamapimod, a p38 MAP kinase inhibitor, in a double-blind, methotrexate-controlled study of patients with active rheumatoid arthritis. Arthritis Rheum 2009, 60:335-344.
5. Genovese M.C. Inhibition of p38: has the fat lady sung?. Arthritis Rheum 2009, 60:317-320.
6. Hopkins A.L. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008, 4:682-690.
7. Janga S.C., Tzakos A. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. Mol Biosyst 2009 Dec, 5(12):1536-1548.
8. Berger S.I., Iyengar R. Network analyses in systems pharmacology. Bioinformatics 2009, 25:2466-2472.
9. Rajasethupathy P., Vayttaden S.J., Bhalla U.S. Systems modeling: a pathway to drug discovery. Curr Opin Chem Biol 2005, 9:400-406.
10. van der Greef J., McBurney R.N. Innovation: rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat Rev Drug Discov 2005, 4:961-967.
11. Wist A.D., Berger S.I., Iyengar R. Systems pharmacology and genome medicine: a future perspective. Genome Med 2009, 1:11.
12. Chung Y.J., Kim T.M., Kim D.W., Namkoong H., Kim H.K., Ha S.A., Kim S., Shin S.M., Kim J.H., Lee Y.J., et al. Gene expression signatures associated with the resistance to imatinib. Leukemia 2006, 20:1542-1550.
13. Kim D.H., Sriharsha L., Xu W., Kamel-Reid S., Liu X., Siminovitch K., Messner H.A., Lipton J.H. Clinical relevance of a pharmacogenetic approach using multiple candidate genes to predict response and resistance to imatinib therapy in chronic myeloid leukemia. Clin Cancer Res 2009, 15:4750-4758.
14. Penny M.A., McHale D. Pharmacogenomics and the drug discovery pipeline: when should it be implemented?. Am J Pharmacogenomics 2005, 5:53-62.
15. Bauer-Mehren A., Furlong L.I., Sanz F. Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 2009, 5:290.
16. Albeck J.G., MacBeath G., White F.M., Sorger P.K., Lauffenburger D.A., Gaudet S. Collecting and organizing systematic sets of protein data. Nat Rev Mol Cell Biol 2006, 7:803-812.
17. Espelin CW, Goldsipe A, Sorger PK, Lauffenburger DA, de Graaf D, Hendriks BS: Elevated GM-CSF and IL-1beta levels compromise the ability of p38 MAPK inhibitors to modulate TNFalpha levels in the human monocytic/macrophage U937 cell line. Mol Syst Biol 2010, doi:10.1039/c002848g.
18. Mitsos A., Melas I.N., Siminelakis P., Chairakaki A.D., Saez-Rodriguez J., Alexopoulos L.G. Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput Biol 2009, 5:e1000591.
19. Cosgrove B.D., Alexopoulos L.G., Saez-Rodriguez J., Griffith L.G., Lauffenburger D.A. A multipathway phosphoproteomic signaling network model of idiosyncratic drug- and inflammatory cytokine-induced toxicity in human hepatocytes. Conf Proc IEEE Eng Med Biol Soc 2009, 1:5452-5455.
20. Cosgrove B.D., King B.M., Hasan M.A., Alexopoulos L.G., Farazi P.A., Hendriks B.S., Griffith L.G., Sorger P.K., Tidor B., Xu J.J., et al. Synergistic drug-cytokine induction of hepatocellular death as an in vitro approach for the study of inflammation-associated idiosyncratic drug hepatotoxicity. Toxicol Appl Pharmacol 2009, 237:317-330.
21. Gaudet S., Janes K.A., Albeck J.G., Pace E.A., Lauffenburger D.A., Sorger P.K. A compendium of signals and responses triggered by prodeath and prosurvival cytokines. Mol Cell Proteomics 2005, 4:1569-1590.
22. Natarajan M., Lin K.M., Hsueh R.C., Sternweis P.C., Ranganathan R. A global analysis of cross-talk in a mammalian cellular signalling network. Nat Cell Biol 2006, 8:571-580.
23. Blagoev B., Ong S.E., Kratchmarova I., Mann M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nat Biotechnol 2004, 22:1139-1145.
24. Schmelzle K., Kane S., Gridley S., Lienhard G.E., White F.M. Temporal dynamics of tyrosine phosphorylation in insulin signaling. Diabetes 2006, 55:2171-2179.
25. Wolf-Yadlin A., Kumar N., Zhang Y., Hautaniemi S., Zaman M., Kim H.D., Grantcharova V., Lauffenburger D.A., White F.M. Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Mol Syst Biol 2006, 2:54.
26. Klamt S., Saez-Rodriguez J., Lindquist J.A., Simeoni L., Gilles E.D. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 2006, 7:56.
27. Leclercq J., Dumont J.E. Boolean analysis of cell regulation networks. J Theor Biol 1983, 104:507-534.
28. Saez-Rodriguez J., Simeoni L., Lindquist J.A., Hemenway R., Bommhardt U., Arndt B., Haus U.U., Weismantel R., Gilles E.D., Klamt S., et al. A logical model provides insights into T cell receptor signaling. PLoS Comput Biol 2007, 3:e163.
29. Samaga R., Saez-Rodriguez J., Alexopoulos L.G., Sorger P.K., Klamt S. The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput Biol 2009, 5:e1000438.
30. Janes K.A., Yaffe M.B. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7:820-828.
31. Aldridge B.B., Burke J.M., Lauffenburger D.A., Sorger P.K. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006, 8:1195-1203.
32. Huang C.Y., Ferrell J.E. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci U S A 1996, 93:10078-10083. PMID: 8816754.
33. Asthagiri A.R., Lauffenburger D.A. A computational study of feedback effects on signal dynamics in a mitogen-activated protein kinase (MAPK) pathway model. Biotechnol Prog 2001, 17:227-239.
34. Kholodenko B.N., Demin O.V., Moehren G., Hoek J.B. Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 1999, 274:30169-30181.
35. Schoeberl B., Eichler-Jonsson C., Gilles E.D., Muller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002, 20:370-375.
36. Hendriks B.S., Cook J., Burke J.M., Beusmans J.M., Lauffenburger D.A., de Graaf D. Computational modelling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity. Syst Biol (Stevenage) 2006, 153:22-33.
37. Birtwistle M.R., Hatakeyama M., Yumoto N., Ogunnaike B.A., Hoek J.B., Kholodenko B.N. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 2007, 3:144.
38. Hatakeyama M., Kimura S., Naka T., Kawasaki T., Yumoto N., Ichikawa M., Kim J.H., Saito K., Saeki M., Shirouzu M., et al. A computational model on the modulation of mitogen-activated protein kinase (MAPK) and Akt pathways in heregulin-induced ErbB signalling. Biochem J 2003, 373:451-463.
39. Chen W.W., Schoeberl B., Jasper P.J., Niepel M., Nielsen U.B., Lauffenburger D.A., Sorger P.K. Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 2009, 5:239.
40. Park C.S., Schneider I.C., Haugh J.M. Kinetic analysis of platelet-derived growth factor receptor/phosphoinositide 3-kinase/Akt signaling in fibroblasts. J Biol Chem 2003, 278:37064-37072.
41. Chaudhri V.K., Kumar D., Misra M., Dua R., Rao K.V. Integration of a phosphatase cascade with the mitogen-activated protein kinase pathway provides for a novel signal processing function. J Biol Chem 2010 Jan 8, 285(2):1296-1310.
42. Sedaghat A.R., Sherman A., Quon M.J. A mathematical model of metabolic insulin signaling pathways. Am J Physiol Endocrinol Metab 2002, 283:E1084-1101.
43. Borisov N., Aksamitiene E., Kiyatkin A., Legewie S., Berkhout J., Maiwald T., Kaimachnikov N.P., Timmer J., Hoek J.B., Kholodenko B.N. Systems-level interactions between insulin-EGF networks amplify mitogenic signaling. Mol Syst Biol 2009, 5:256.
44. Lee E., Salic A., Kruger R., Heinrich R., Kirschner M.W. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol 2003, 1:E10.
45. Hendriks B.S., Hua F., Chabot J.R. Analysis of mechanistic pathway models in drug discovery: p38 pathway. Biotechnol Prog 2008, 24:96-109.
46. Hoffmann A., Levchenko A., Scott M.L., Baltimore D. The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation. Science 2002, 298:1241-1245.
47. Cheong R., Hoffmann A., Levchenko A. Understanding NF-kappaB signaling via mathematical modeling. Mol Syst Biol 2008, 4:192.
48. Shankaran H., Wiley H.S. Smad signaling dynamics: insights from a parsimonious model. Sci Signal 2008, 1:e41.
49. Nakakuki T., Birtwistle M.R., Saeki Y., Yumoto N., Ide K., Nagashima T., Brusch L., Ogunnaike B.A., Okada-Hatakeyama M., Kholodenko B.N. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 2010 May 28, 141(5):884-896.
50. Fussenegger M., Bailey J.E., Varner J. A mathematical model of caspase function in apoptosis. Nat Biotechnol 2000, 18:768-774.
51. Bentele M., Lavrik I., Ulrich M., Stosser S., Heermann D.W., Kalthoff H., Krammer P.H., Eils R. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol 2004, 166:839-851.
52. Hua F., Cornejo M.G., Cardone M.H., Stokes C.L., Lauffenburger D.A. Effects of Bcl-2 levels on Fas signaling-induced caspase-3 activation: molecular genetic tests of computational model predictions. J Immunol 2005, 175:985-995.
53. Albeck J.G., Burke J.M., Spencer S.L., Lauffenburger D.A., Sorger P.K. Modeling a snap-action, variable-delay switch controlling extrinsic cell death. PLoS Biol 2008, 6:2831-2852.
54. Hatzimanikatis V., Lee K.H., Bailey J.E. A mathematical description of regulation of the G1-S transition of the mammalian cell cycle. Biotechnol Bioeng 1999, 65:631-637.
55. Hendriks B.S., Griffiths G.J., Benson R., Kenyon D., Lazzara M., Swinton J., Beck S., Hickinson M., Beusmans J.M., Lauffenburger D., et al. Decreased internalisation of erbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib. Syst Biol (Stevenage) 2006, 153:457-466.
56. Hendriks B.S., Seidl K.M., Chabot J.R. Two additive mechanisms impair the differentiation of 'substrate-selective' p38 inhibitors from classical p38 inhibitors in vitro. BMC Syst Biol. 2010 Mar 15, 4:23.
57. Chassagnole C., Jackson R.C., Hussain N., Bashir L., Derow C., Savin J., Fell D.A. Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development. Biosystems 2006, 83:91-97.
58. Schoeberl B., Pace E.A., Fitzgerald J.B., Harms B.D., Xu L., Nie L., Linggi B., Kalra A., Paragas V., Bukhalid R., et al. Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2009, 2:ra31.
59. Schilling M., Maiwald T., Hengl S., Winter D., Kreutz C., Kolch W., Lehmann W.D., Timmer J., Klingmüller U. Theoretical and experimental analysis links isoform-specific ERK signalling to cell fate decisions. Mol Syst Biol 2009, 5:334. Epub 2009 Dec 22. PMID: 20029368.