High-Throughput Protein-Based Technologies and Computational Models for Drug Development, Efficacy, and Toxicity

Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools

Volumn , Issue , 2008, Pages 29-52

  Alexopoulos, Leonidas G ^a,b,c   Saez Rodriguez, Julio ^a,b   Espelin, Christopher W ^d  

^a HARVARD MEDICAL SCHOOL   (United States)

^b Massachusetts Institute of Technology Cambridge   (United States)

^c NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^d PFIZER INC   (United States)

Author keywords

Experimental platforms for protein activity quantification; Genomics technologies; Organizing and analyzing data

Indexed keywords

EID: 84889873268     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470431818.ch2     Document Type: Chapter

References (64)

1. Csermely P, Agoston V, Pongor S. T he efficiency of multi -t arget drugs: the network approach might help drug design. Trends Pharmacol Sci 2005; 26: 178-82.
2. Araujo RP, Liotta LA, Petricoin EF. Proteins, drug targets and the mechanisms they control: the simple truth about complex networks. Nat Rev Drug Discov 2007; 6: 871-80.
3. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100: 57-70.
4. Koch WH. Technology platforms for pharmacogenomic diagnostic assays. Nat Rev Drug Discov 2004; 3: 749-61.
5. Teufel A, Staib F, Kanzler S, Weinmann A, Schulze-Bergkamen H, Galle PR. Genetics of hepatocellular carcinoma. World J Gastroenterol 2007; 13: 2271-82.
6. Wilkens L, Bredt M, Flemming P, Klempnauer J, Heinrich Kreipe H. Differentiation of multicentric origin from intra-organ metastatic spread of hepatocellular carcinomas by comparative genomic hybridization. J Pathol 2000; 192: 43-51.
7. Bosch FX, Ribes J, Borras J. Epidemiology of primary liver cancer. Semin Liver Dis 1999; 19: 271-85.
8. Abou-Alfa GK. Hepatocellular carcinoma: molecular biology and therapy. Semin Oncol 2006; 33: S79-83.
9. Thorgeirsson SS, Grisham JW. Molecular pathogenesis of human hepatocellular carcinoma. Nat Genet 2002; 31: 339-46.
10. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004; 350: 2129-39. Epub 004 Apr 29.
11. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to Gefitinib therapy. Science 2004; 304: 1497-500.
12. Ingelman-Sundberg M, Oscarson M, McLellan RA. Polymorphic human cytochrome P450 enzymes: an opportunity for individualized drug treatment. Trends Pharmacol Sci 1999; 20: 342-9.
13. Desta Z, Zhao X, Shin JG, Flockhart DA. Clinical significance of the cytochrome P450 2C19 genetic polymorphism. Clin Pharmacokinet 2002; 41: 913-58.
14. Syvanen AC. Toward genome-wide SNP genotyping. Nat Genet 2005; 37: S5-10.
15. Bumol TF, Watanabe AM. Genetic information, genomic technologies, and the future of drug discovery. JAMA 2001; 285: 551-5.
16. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999; 286: 487-91.
17. Evans WE, McLeod HL. Pharmacogenomics-drug disposition, drug targets, and side effects. N Engl J Med 2003; 348: 538-49.
18. Phillips KA, Veenstra DL, Oren E, Lee JK, Sadee W. Potential role of pharmacogenomics in reducing adverse drug reactions: a systematic review. JAMA 2001; 286: 2270-9.
19. Kim HL. Comparison of oligonucleotide-microarray and serial analysis of gene expression (SAGE) in transcript profiling analysis of megakaryocytes derived from CD34+ cells. Exp Mol Med 2003; 35: 460-6.
20. Committee on Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment NRC. Applications of toxicogenomic technologies to predictive toxicology and risk assessment. Washington, DC: National Academies Press, 2007.
21. Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, Wu B, et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res 2006; 16: 383-93.
22. Jabbari K, Bernardi G. Cytosine methylation and CpG, TpG (CpA) and TpA frequencies. Gene 2004; 333: 143-9.
23. Raamsdonk LM, Teusink B, Broadhurst D, Zhang NS, Hayes A, Walsh MC, et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 2001; 19: 45-50.
24. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 2004; 22: 245-52.
25. Haugen AC, Kelley R, Collins JB, Tucker CJ, Deng C, Afshari CA, et al. Integrating phenotypic and expression profiles to map arsenic-response networks. Genome Biol 2004; 5: R95. Epub 2004 Nov 29.
26. Anderson NL, Anderson NG. The human plasma proteome-history, character, and diagnostic prospects. Mol Cell Proteomics 2002; 1: 845-67.
27. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 2003; 422: 198-207.
28. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001; 292: 929-34.
29. Pandey A, Mann M. Proteomics to study genes and genomes. N ature 2000; 405: 837-46.
30. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999; 17: 994-9.
31. Mann M, Jensen ON. Proteomic analysis of post-translational modifications. Nat Biotechnol 2003; 21: 255-61.
32. Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006; 8: 1195-203.
33. Albeck JG, MacBeath G, White FM, Sorger PK, Lauffenburger DA, Gaudet S. Collecting and organizing systematic sets of protein data. Nat Rev Mol Cell Biol 2006; 7: 803-12.
34. Janes KA, Albeck JG, Peng LX, Sorger PK, Lauffenburger DA, Yaffe MB. A high-throughput quantitative multiplex kinase assay for monitoring information flow in signaling networks: application to sepsis-apoptosis. Mol Cell Proteomics 2003; 2: 463-73.
35. Pregibon DC, Toner M, Doyle PS. Multifunctional encoded particles for high -throughput biomolecule analysis. Science 2007; 315: 1393-6.
36. Knezevic V. Proteomic profiling of the cancer microenvironment by antibody arrays. Proteomics 2001; 1: 1271-8.
37. Fredriksson S, Gullberg M, Jarvius J, Olsson C, Pietras K, Gustafsdottir SM, et al. Protein detection using proximity-dependent DNA ligation assays. Nat Biotech 2002; 20: 473-7.
38. Gustafsdottir SM, Schlingemann J, Rada-Iglesias A, Schallmeiner E, Kamali-Moghaddam M, Wadelius C, et al. In vitro analysis of DNA-protein interactions by proximity ligation. Proc Natl Acad Sci USA 2007; 104: 3067-72.
39. Schallmeiner E, Oksanen E, Ericsson O, Spangberg L, Eriksson S, Stenman U-H, et al. Sensitive protein detection via triple-binder proximity ligation assays. Nat Meth 2007; 4: 135-7.
40. Soderberg O, Gullberg M, Jarvius M, Ridderstrale K, Leuchowius K-J, Jarvius J, et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat Meth 2006; 3: 995-1000.
41. Alexopoulos LG, Saez-Rodriguez J, Cosgrove B, Lauffenburger DA, Sorger PK. Context sensitive pathway maps of primary and transformed human hepatocytes reveal profound alterations in NfkB signaling. In preparation, 2008.
42. Shah AR, Singhal M, Klicker KR, Stephan EG, Wiley HS, Waters KM. Enabling high-throughput data management for systems biology: the Bioinformatics Resource Manager. Bioinformatics 2007; 23: 906-9.
43. Saez-Rodriguez J, Goldsipe A, Muhlich J, Alexopoulos LG, Millard B, L auffenburger DA, et al. Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics 2008; 24: 840-7.
44. Klamt S, Saez-Rodriguez J, Gilles E. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Sys Biol 2007; 1: 2.
45. Janes KA, Yaffe MB. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006; 7: 820-8.
46. Tarca AL, Carey VJ, Chen XW, Romero R, Draghici S. Machine learning and its applications to biology. PLoS Comput Biol 2007; 3: e116.
47. Alexopoulos LG, Saez-Rodriguez J, Lauffenburger DA, Sorger PK. Support vector machine approach for classification of drug toxicity based on their effects on the intracellular and extracellular signaling motifs. In preparation, 2008.
48. Espelin C, Goldsipe A, Sorger PK, Lauffenburger DA, de Graaf D, Hendriks BS. Multidimensional phosphoprotein and cytokine profiling reveals context -dependent behavior of kinase inhibitors in U937 macrophages. In preparation, 2008.
49. Cho KH, Choo SM, Jung SH, Kim JR, Choi HS, Kim J. Reverse engineering of gene regulatory networks. Iet Sys Biol 2007; 1: 149-63.
50. Sachs K, Perez O, Pe ' er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005; 308: 523-9.
51. Santos SDM, Verveer PJ, Bastiaens PIH. Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat Cell Biol 2007; 9: 324-30.
52. Aittokallio T, Schwikowski B. Graph-based methods for analysing networks in cell biology. Brief Bioinform 2006; 7: 243-55.
53. Barabasi A-L, Oltvai ZN. Network biology: understanding the cell ' s functional organization. Nat Rev Genet 2004; 5: 101-13.
54. Klamt S, Saez-Rodriguez J, Lindquist JA, Simeoni L, Gilles ED. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinform 2006; 7: 56.
55. Jaqaman K, Danuser G. Linking data to models: data regression. Nat Rev Mol Cell Biol 2006; 7: 813-9.
56. Kremling A, Saez-Rodriguez J. Systems biology-an engineering perspective. J Biotechnol 2007; 129: 329-51.
57. de Jong H, Geiselmann J, Hernandez C, Page M. Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 2003; 19: 336-44.
58. Gonzalez AG, Naldi A, Sanchez L, Thieffry D, Chaouiya C. GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. Biosystems 2006; 84: 91-100.
59. Vacheva I, Eils R. Computational systems biology platforms. IT Info Technol 2006; 48: 140-47.
60. Alves R, Antunes F, Salvador A. Tools for kinetic modeling of biochemical networks. Nat Biotech 2006; 24: 667-72.
61. Hucka M, Finney A, Sauro HM, Bolouri H, D oyle JC, Kitano H, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003; 19: 524-31.
62. Gaudet S, Janes KA, Albeck JG, Pace EA, Lauffenburger DA, Sorger PK. A compendium of signals and responses triggered by prodeath and prosurvival cytokines. Mol Cell Proteomics 2005; 4: 1569-90.
63. Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 2005; 310: 1646-53.
64. Hendriks BS, Hua F, Chabot JR. Analysis of mechanistic pathway models in drug discovery: p38 pathway. Biotechnol Prog 2008; 24: 96-109.