Drug discovery and design for complex diseases through QSAR computational methods

Current Pharmaceutical Design

Volumn 16, Issue 24, 2010, Pages 2640-2655

  Munteanu, Cristian R ^a   Fernández Blanco, Enrique ^a   Seoane, José A ^a   Izquierdo Novo, Pilar ^b   Rodríguez Fernández, José Ángel ^c   Prieto González, José María ^d   Rabuñal, Juan R ^a   Pazos, Alejandro ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

^b Centro Oncologico de Galicia   (Spain)

^c HOSPITAL JUAN CANALEJO   (Spain)

^d Hospital Clinico de Santiago de Compostela   (Spain)

Author keywords

Complex disease; Complex network; Drug design; Graphs; QSAR

Indexed keywords

1 AMINOINDAN DERIVATIVE; 1 AMINOTETRALINE DERIVATIVE; 2 AMINOTETRALIN DERIVATIVE; 3,4 QUINAZOLINEDIOL DERIVATIVE; ANGIOTENSIN ANTAGONIST; BENZODIAZEPINE DERIVATIVE; CALCIUM CHANNEL BLOCKING AGENT; CATHEPSIN B INHIBITOR; CHOLINESTERASE INHIBITOR; CORTICOTROPIN RELEASING FACTOR ANTAGONIST; FLAVONOID; GLYCOGEN SYNTHASE KINASE 3 INHIBITOR; INDIRUBIN; KYS 05090; PHENETHYLAMINE CARBAMATE DERIVATIVE; PIPERAZINE DERIVATIVE; SEROTONIN 6 ANTAGONIST; TAXANE DERIVATIVE; TRIFLUORO 3 AMINO 2 PROPANOL DERIVATIVE; UNCLASSIFIED DRUG;

ARTICLE; CARDIOLOGY; DRUG DESIGN; DRUG RESEARCH; DRUG SCREENING; NEUROLOGY; ONCOLOGY; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

CORONARY DISEASE; DRUG DESIGN; DRUG DISCOVERY; DRUG THERAPY; HUMANS; MODELS, BIOLOGICAL; MODELS, MOLECULAR; MOLECULAR CONFORMATION; NEOPLASMS; NERVOUS SYSTEM DISEASES; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77957962930     PISSN: 13816128     EISSN: None     Source Type: Journal    
DOI: 10.2174/138161210792389252     Document Type: Article

References (264)

1. Lander ES, Schork NJ. Genetic dissection of complex traits. Science 1994; 265: 2037-48.
2. Kiberstis P, Roberts L. It's not just the genes. Science 2002; 296: 685.
3. Farrer MJ. Genetics of Parkinson disease: paradigm shifts and future prospects. Nat Rev Genet 2006; 7: 306-18.
4. McDonnell SK, Schaid DJ, Elbaz A, et al. Complex segregation analysis of Parkinson's disease: The Mayo Clinic Family Study. Ann Neurol 2006; 59: 788-95.
5. Delles C. Candidate gene studies in cardiovascular medicine: complex diseases and even more complex intermediate phenotypes. J Hypertension 2008; 26: 1069-71.
6. Merikangas KR, Risch N. Genomic priorities and public health. Science 2003; 302: 599-601.
7. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science 1996; 273: 1516-7.
8. Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004; 5: 101-13.
9. Bornholdt S, Schuster HG. Handbook of graphs and complex networks: from the genome to the internet. Wheinheim: Wiley-VCH GmbH & CO KGa 2003.
10. Todeschini R, Consonni V. Handbook of molecular descriptors. USA: Wiley-VCH 2002.
11. Mandado M, Gonzáles-Moa MJ, Mosquera RA. Chemical graph theory and n-center electron delocalization indices: A study on polycyclic aromatic hydrocarbons. J Comput Chem 2007; 28: 1625-33.
12. González-Díaz H, Ferino G, Podda G, Uriarte E. Discriminating prostate cancer patients from control group with connectivity indices. ECSOC 2007; 11: G1:1-10.
13. González-Díaz H, Vina D, Santana L, de Clercq E, Uriarte E. Stochastic entropy QSAR for the in silico discovery of anticancer compounds: prediction, synthesis, and in vitro assay of new purine carbanucleosides. Bioorg Med Chem 2006; 14: 1095-107.
14. Chou KC. Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys Chem 1990; 35: 1-24.
15. Chou KC. Graphical rules in steady and non-steady enzyme kinetics. J Biol Chem 1989; 264: 12074-79.
16. Chou KC. Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys Chem 1990; 35: 1-24.
17. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005; 21: 10-9.
18. Chou KC, Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem J 1980; 187: 829-35.
19. Randi M, Vrako M, Nandy A, Basak SC. On 3-D graphical representation of DNA primary sequences and their numerical characterization. J Chem Inf Comput Sci 2000; 40: 1235-44.
20. González-Díaz H, Vilar S, Santana L, Uriarte E. Medicinal chemistry and bioinformatics -current trends in drugs discovery with networks topological indices. Curr Top Med Chem 2007; 7: 1025-39.
21. Estrada E. Application of a novel graph-theoretic folding degree index to the study of steroid-DB3 antibody binding affinity. Comput Biol Chem 2003; 27: 305-13.
22. Liao B, Wang TM. New 2D graphical representation of DNA sequences. J Comput Chem 2004; 25: 1364-8.
23. Chou KC. Review: Structural bioinformatics and its impact to biomedical science. Curr Med Chem 2004; 11: 2105-34.
24. Liao B, Xiang X, Zhu W. Coronavirus phylogeny based on 2D graphical representation of DNA sequence. J Comput Chem 2006; 27: 1196-202.
25. Randic M. Condensed representation of DNA primary sequences. J Chem Inf Comput Sci 2000; 40: 50-6.
26. Randic M, Balaban AT. On a four-dimensional representation of DNA primary sequences. J Chem Inf Comput Sci 2003; 43: 532-9.
27. Perez MA, Sanz MB, Torres LR, Avalos RG, Gonzalez MP, Diaz HG. A topological sub-structural approach for predicting human intestinal absorption of drugs. Eur J Med Chem 2004; 39: 905-16.
28. Agüero-Chapin G, Gonzalez-Diaz H, Molina R, Varona-Santos J, Uriarte E, Gonzalez-Diaz Y. Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS lett 2006; 580: 723-30.
29. González-Díaz H, Cruz-Monteagudo M, Molina R, Tenorio E, Uriarte E. Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model. Bioorg Med Chem 2005; 13: 1119-29.
30. Kier LB, Bonchev D, Buck GA. Modeling biochemical networks: a cellular-automata approach. Chem Biodivers 2005; 2: 233-43.
31. Loftsson T, Thorsteinsson T, Masson M. Hydrolysis kinetics and QSAR investigation of soft antimicrobial agents. J Pharm Pharmacol 2005; 57: 721-7.
32. Mekenyan OG, Dimitrov SD, Pavlov TS, Veith GD. A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework. Curr Pharm Des 2004; 10: 1273-93.
33. Rush TS, 3rd, Grant JA, Mosyak L, Nicholls A. A shape-based 3-D scaffold hopping method and its application to a bacterial proteinprotein interaction. J Med Chem 2005; 48: 1489-95.
34. Chou KC, Cai YD. Predicting protein-protein interactions from sequences in a hybridization space. J Proteome Res 2006; 5: 316-22.
35. Chen XW, Liu M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 2005; 21: 4394-400.
36. Chen R, Weng Z. A novel shape complementarity scoring function for protein-protein docking. Proteins 2003; 51: 397-408.
37. Bonchev D. Complexity analysis of yeast proteome network. Chem Biodivers 2004; 1: 312-26.
38. J.F. Sharom DSB, M. Tyers. From large networks to small molecules. Curr Opin Chem Biol 2004; 8: 81-90.
39. Andraos J. Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Canadian J Chem 2008; 86: 342-57.
40. Munteanu CR, Magalhaes AL, Uriarte E, Gonzalez-Diaz H. Multitarget QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J Theor Biol 2009; 257: 303-11.
41. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 2009; 261: 449-58.
42. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008; 29: 2613-22.
43. González-Díaz H, Ferino G, Prado-Prado FJ., In: Cho WCS, Ed. Protein graphs in cancer prediction. USA: Springer 2010.
44. Ferino G, Delogu G, Podda G, Uriarte E, González-Díaz H. In: Brian H. Mitchem, Charles L. Sharnham, Ed. Quantitative proteome-disease relationships (QPDRs) in clinical chemistry: prediction of prostate cancer with spectral moments of PSA/MS star networks. NY: Nova Science Publisher 2009.
45. Devillers J, Balaban AT. Topological indices and related descriptors in QSAR and QSPR. The Netherlands: Gordon and Breach 1999.
46. Barabasi AL, Bonabeau E. Scale-free networks. Sci Am 2003; 288: 60-9.
47. Balaban AT, Basak SC, Beteringhe A, Mills D, Supuran CT. QSAR study using topological indices for inhibition of carbonic anhydrase II by sulfanilamides and Schiff bases. Mol Divers 2004; 8: 401-12.
48. Barabasi AL. Sociology. Network theory-the emergence of the creative enterprise. Science 2005; 308: 639-41.
49. Ferino G, Gonzalez-Diaz H, Delogu G, Podda G, Uriarte E. Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer. Biochem Biophys Res Commun 2008; 372: 320-5.
50. Gonzalez-Diaz H, Gonzalez-Diaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008; 8: 750-78.
51. Randic M, Zupan J, Vikic-Topic D. On representation of proteins by star-like graphs. J Mol Graph Model 2007; 290-305.
52. Parvu L. QSAR -a piece of drug design. J Cell Mol Med 2003; 7: 333-5.
53. Raevsky OA. Physicochemical descriptors in property-based drug design. Mini Rev Med Chem 2004; 4: 1041-52.
54. Roy K. Topological descriptors in drug design and modeling studies. Mol Divers 2004; 8: 321-3.
55. Kellogg GE, Semus SF. 3D QSAR in modern drug design. EXS 2003; 223-41.
56. Klebe G. Recent developments in structure-based drug design. J Mol Med 2000; 78: 269-81.
57. Ivanciuc O. In: RA Meyers, Ed. Drug design with artificial neural networks. New York: Springer-Verlag 2009.
58. Gonzalez-Diaz H, Perez-Montoto LG, Duardo-Sanchez A, et al. Generalized lattice graphs for 2D-visualization of biological information. J Theor Biol 2009; 261: 136-47.
59. Borman S. New QSAR techniques eyed for environmental assessments. Chem Eng News 1990; 68: 20-23.
60. Lipnick RL. Charles ernest overton: narcosis studies and a contribution to general pharmacology. Trends Pharmacol Sci 1986; 7: 161-64.
61. Hansch C, Leo A, Taft RW. A survey of hammett substituent constants and resonance and field parameters. Chem Rev 1991; 91: 165-95.
62. Cramer RD, Milne M. Meeting computer chemistry section. Am Chem Soc 1979; Abstr no. 44.
63. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988; 110: 5959-67.
64. Leach AR. Molecular modelling: principles and applications. N.J.: Prentice Hall 2001.
65. Klebe G, Abraham U, Mietzner T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 1994; 37: 4130-46.
66. Bohm M, St. Rzebecher J, Klebe G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 1999; 42: 458-77.
67. HQSARTM Manual, SYBYL 6.9.2, Tripos Inc., St. Louis, MO, 2003.
68. Tong W, Lowis DR, Perkins R, et al. Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sci 1998; 38: 669-77.
69. MARCH-INSIDE version 3.0 (Markov chains invariants for simulation & design); Windows supported version under request to the main author contact email: gonzalezdiazh@yahoo.es, 2007.
70. DRAGON for Windows (software for molecular descriptor calculations), http://www.talete.mi.it., 2005.
71. S2SNet -Sequence to Star Network, Reg. No. 03/2008/1338, Santiago de Compostela, Spain, 2008.
72. CentiBiN Version 1.4.2, 2006.
73. Pajek, Program for Large Network Analysis (ver. 1.15), http://vlado.fmf.uni-lj.si/pub/networks/pajek/, 2006.
74. Balaban AT. Chemical graphs. XXXIV. Five new topological indices for the branching of tree-like graphs. Theor. Chim. Acta 1979; 53: 355-75.
75. Bonchev D, Rouvray DH. Chemical graph theory. NewYork: Gordon & Breach 1991.
76. Gutman I, Ruscic B, Trinajstic N, Wilcox Jr. CF. Graph Theory and molecular orbitals. XII. Acyclic Polyenes. J Chem Phys 1975; 62: 3399-405.
77. Pogliani L. Modeling with special descriptors derived from a medium-sized set of connectivity indices. J Phys Chem 1996; 100: 18065-77.
78. Platt JR. Influence of neighbor bonds on additive bond properties in paraffins. J Chem Phys 1947; 15: 419-20.
79. Schultz HP, Schultz EB, Schultz TP. Topological organic chemistry. 4. Graph theory, matrix permanents and topological indices of alkanes. J Chem Inf Comput Sci 1992; 32: 69-72.
80. Balaban AT. Topological indices based on topological distances in molecular graphs. Pure Appl Chem 1983; 55: 199-206.
81. Ivanciuc O, Balaban TS, Balaban AT. Design of topological indices. Part 4. Reciprocal distance matrix, related local vertex invariants and topological indices. J Math Chem 1993; 12: 309-18.
82. Diudea MV, Topan M, Graovac A. Molecular topology. 17. Layer matrices of walk degrees. J Chem Inf Comput Sci 1994; 34: 1072-78.
83. Rücker C, Rücker G. Mathematical relation between extended connectivity and eigenvector coefficients. J Chem Inf Comput Sci 1994; 34: 534-38.
84. Randic M. Random walks and their diagnostic value for characterization of atomic environment. J Comput Chem 1980; 1: 386-99.
85. Balaban AT. Graph theory and topology in chemistry. Amsterdam: Elsevier 1987.
86. Kier LB, Hall LH. Molecular connectivity in structure-activity analysis. Chichetser (UK): RSP-Wiley 1986.
87. Bonchev D. Information theoretic indices for characterization of chemical structures. chichester: Research Studies Press 1983.
88. Balaban AT, Balaban TS. New vertex invariants and topological indices of chemical graphs based on information on distances. J Math Chem 1991; 8: 383-97.
89. Broto P, Devillers J. In: Karcher W, Devillers J, Eds. Practical applications of quantitative structure-activity relationships (QSAR). Dordrecht: Kluwer 1990; pp. 105-27.
90. Estrada E. Edge adjacency relationships and a novel topological index related to molecular volume. J Chem Inf Comput Sci 1995; 35: 31-3.
91. Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs, 1. definition and applications to the prediction of physical properties of alkanes. J Chem Inf Comput Sci 1996; 36: 844-49.
92. Burden FR. Molecular identification number for substructure searches. J Chem Inf Comput Sci 1989; 29: 225-27.
93. Benigni R, Passerini L, Pino A, Giuliani A. The information content of the eigenvalues from modified adjacency matrices: large scale and small scale correlations. Quant Struct -Act Relat 1999; 15: 449-55.
94. Galvez J, Garcia R, Salabert MT, Soler R. Charge indexes: new topological descriptors. Chem Inf Comput Sci 1994; 34: 520-25.
95. Balaban AT, Ciubotariu D, Medeleanu M. Topological indices and real number vertex invariants based on graph eigenvalues or eigenvectors. Chem Inf Comput Sci 1991; 31: 517-23.
96. Randic M. Molecular profiles. Novel geometry-dependent molecular descriptors. New J Chem 1995; 19: 781-91.
97. Balaban AT. From chemical topology to 3D. geometry. J Chem Inf Comput Sci 1997; 37: 645-50.
98. Randic M, Kleiner AF, DeAlba LM. Distance/distance matrices. J Chem Inf Comput Sci 1994; 34: 277-86.
99. Hemmer MC, Steinhauer V, Gasteiger J. Deriving the 3D structure of organic molecules from their infrared spectra. Vibrat Spect 1999; 19: 151-64.
100. Gasteiger J, Sadowski J, Schuur J, Selzer P, Steinhauer L, Steinhauer V. Chemical information in 3D space. Chem Inf Comput Sci 1996; 36: 1030-37.
101. Todeschini R, Lasagni M, Marengo E. New molecular descriptors for 2D-and 3D-structures. Theory J Chemometrics 1994; 8: 263-73.
102. Consonni V, Todeschini R, Pavan M. Part 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 2002; 42: 682-92.
103. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 1989; 29: 163-72.
104. Karelson M, Lobanov VS, Katritzky AR. Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 1996; 96: 1027-43.
105. Carhart RE, Smith DH, Ventakataraghavan R. 2D frequency fingerprints. J Chem Inf Comput Sci 1985; 25: 64-73.
106. Giusti I, D'Ascenzo S, Millimaggi D, et al. Cathepsin B mediates the pH-dependent proinvasive activity of tumor-shed microvesicles. Neoplasia 2008; 10: 481-8.
107. Ha SD, Martins A, Khazaie K, Han J, Chan BM, Kim SO. Cathepsin B is involved in the trafficking of TNF-alpha-containing vesicles to the plasma membrane in macrophages. J Immunol 2008; 181: 690-97.
108. Downs LS, Jr., Lima PH, Bliss RL, Blomquist CH. Cathepsins B and D activity and activity ratios in normal ovaries, benign ovarian neoplasms, and epithelial ovarian cancer. J Soc Gynecol Investig 2005; 12: 539-44.
109. Otto H-H, Schirmeister T. Cysteine proteases and their inhibitors. Chem Rev 1997; 97: 133-72.
110. Zhou Z, Wang Y, Bryant SH. QSAR models for predicting cathepsin B inhibition by small molecules-Continuous and binary QSAR models to classify cathepsin B inhibition activities of small molecules. J Mol Graph Model 2010; 28(8): 714-27.
111. Grundke-Iqbal I, Iqbal K, Tung YC, Quinlan M, Wisniewski HM, Binder LI. Abnormal phosphorylation of the microtubule-associated protein tau (tau) in Alzheimer cytoskeletal pathology. Proc Natl Acad Sci USA 1986; 83: 4913-7.
112. Kopke E, Tung YC, Shaikh S, Alonso AC, Iqbal K, Grundke-Iqbal I. Microtubule-associated protein tau. Abnormal phosphorylation of a non-paired helical filament pool in Alzheimer disease. J Biol Chem 1993; 268: 24374-84.
113. De Strooper B, Annaert W. Proteolytic processing and cell biological functions of the amyloid precursor protein. J Cell Sci 2000; 113 (Pt 11): 1857-70.
114. Nunan J, Small DH. Proteolytic processing of the amyloid-beta protein precursor of Alzheimer's disease. Essays Biochem 2002; 38: 37-49.
115. Koo EH, Squazzo SL. Evidence that production and release of amyloid beta-protein involves the endocytic pathway. J Biol Chem 1994; 269: 17386-9.
116. Perez RG, Soriano S, Hayes JD, et al. Mutagenesis identifies new signals for beta-amyloid precursor protein endocytosis, turnover, and the generation of secreted fragments, including Abeta42. J Biol Chem 1999; 274: 18851-6.
117. Sammi T, Silakari O, Ravikumar M. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of various benzodiazepine analogues of gamma-secretase inhibitors. J Mol Model 2009; 15: 343-8.
118. Cohen P. The Enzymes. New York: Academic Press 1986.
119. Lovestone S, Reynolds CH, Latimer D, et al. Alzheimer's disease-like phosphorylation of the microtubule-associated protein tau by glycogen synthase kinase-3 in transfected mammalian cells. Curr Biol 1994; 4: 1077-86.
120. Phiel CJ, Wilson CA, Lee VM, Klein PS. GSK-3 alpha regulates production of Alzheimer's disease amyloid-beta peptides. Nature 2003; 423: 435-39.
121. Cross DAE, Culbert AA, Chalmers KA, Facci L, Skaper SD, Reith AD. Selective small-molecule inhibitors of glycogen synthase kinase-3 activity protect primary neurones from death. J Neurochem 2001; 77: 94-102.
122. Lather V, Kristam R, Saini JS, Kristam R, Karthikeyan NA, Balaji VN. QSAR models for prediction of glycogen synthase kinase-3beta inhibitory activity of indirubin derivatives. QSAR Comb Sci 2008; 27: 718-28.
123. Katritzky AR, Sild S, Karelson M. General quantitative structure-property relationship treatment of the refractive index of organic compounds. J Chem Inf Comput Sci 1998; 38: 840-44.
124. Gao H, Williams C, Labute P, Bajorath J. Binary quantitative structure-activity relationships (qsar) analysis of estrogen receptor ligands. J Chem Inf Comput Sci 1999; 39: 164-68.
125. Katritzky AR, Mu L, Lobanov VS, Karelson M. Correlation of boiling points with molecular structure. I A training set of 298 diverse organics and a test set of 9 simple inorganics. J Phys Chem 1996; 100: 10400-07.
126. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening. 1. Methodology and preliminary results. J Comput Aided Mol Des 2006; 20: 647-71.
127. Butters N, Delis DC, Lucas JA. Clinical assessment of memory disorders in amnesia and dementia. Annu Rev Psychol 1995; 46: 493-523.
128. Giacobini E. Cholinesterases: new roles in brain function and in Alzheimer's disease. Neurochem Res 2003; 28: 515-22.
129. Terry AV, Jr., Buccafusco JJ. The cholinergic hypothesis of age and Alzheimer's disease-related cognitive deficits: recent challenges and their implications for novel drug development. J Pharmacol Exp Ther 2003; 306: 821-7.
130. Soreq H, Zaku H. Human cholinesterases and anticholinesterases. San Diego: Academic Press 1993.
131. Massoulie J, Sussman J, Bon S, Silman I. Structure and functions of acetylcholinesterase and butyrylcholinesterase. Prog Brain Res 1993; 98: 139-46.
132. Cokugras AN. Butyrylcholinesterase: structure and physiological importance. Turk J Biochem 2003; 28: 54-61.
133. Bazelyansky M, Robey E, Kirsch JF. Fractional diffusion-limited component of reactions catalyzed by acetylcholinesterase. Biochemistry 1986; 25: 125-30.
134. Wright C, Geula C, Mesulam M. Neuroglial cholinesterases in the normal brain and in Alzheimer's Disease: Relationship to plaques, tangles, and patterns of selective vulnerability. Ann Neurol 1993; 34: 373-84.
135. Lockridge O, Masson P. Pesticides and susceptible populations: people with butyrylcholinesterase genetic variants may be at risk. Neurotoxicology 2000; 21: 113-26.
136. Massoulie J, Pezzementi L, Bon S, Krejci E, Vallette FM. Molecular and cellular biology of cholinesterases. Prog Neurobiol 1993; 41: 31-91.
137. Xie W, Stribley JA, Chatonnet A, et al. Postnatal developmental delay and supersensitivity to organophosphate in gene-targeted mice lacking acetylcholinesterase. J Pharmacol Exp Ther 2000; 293: 896-902.
138. Zahodne LB, Fernandez HH. Course, prognosis, and management of psychosis in Parkinson's disease: are current treatments really effective? CNS Spectr 2008; 13 (3 Suppl. 4): 26-33.
139. Adam OR, Jankovic J. Symptomatic treatment of huntington disease. Neurotherapeutics 2008; 5: 181-97.
140. Mahadeva B, Phillips LH, 2nd, Juel VC. Autoimmune disorders of neuromuscular transmission. Semin Neurol 2008; 28: 212-27.
141. Stip E, Sepehry AA, Chouinard S. Add-on therapy with acetylcholinesterase inhibitors for memory dysfunction in schizophrenia: a systematic quantitative review, part 2. Clin Neuropharmacol 2007; 30: 218-29.
142. Millard CB, Broomfield CA. Anticholinesterases: medical applications of neurochemical principles. J Neurochem 1995; 64: 1909-18.
143. Christodoulou C, MacAllister WS, McLinskey NA, Krupp LB. Treatment of cognitive impairment in multiple sclerosis: is the use of acetylcholinesterase inhibitors a viable option? CNS Drugs 2008; 22: 87-97.
144. Trujillo-Ferrara J, Correa-Basurto J, Espinosa J, Garcia J. Solventfree synthesis of arylamides and arylimides, analogues of acetylcholine. J Syn Commun 2005; 35: 1-7.
145. Zaheer-ul H, Uddin R, Yuan H, Petukhov PA, Choudhary MI, Madura JD. Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm. J Chem Inf Model 2008; 48: 1092-103.
146. Correa-Basurto J, Flores-Sandoval C, Marvin-Cruz J, Rojo-Dominguez A, Espinoza-Fonseca LM, Trujillo-Ferrara JG. Docking and quantum mechanic studies on cholinesterase and their inhibitors. Eur J Med Chem 2007; 42: 10-19.
147. Solomon KA, Sundararajan S, Abirami V. QSAR studies on N-aryl derivative activity towards Alzheimer's disease. Molecules 2009; 14: 1448-55.
148. Takahashi J, Hijikuro I, Kihara T, et al. Design, synthesis, evaluation and QSAR analysis of N(1)-substituted norcymserine derivatives as selective butyrylcholinesterase inhibitors. Bioorg Med Chem Lett 2010 20: 1718-20.
149. Oprea T. Property distribution of drug-related chemical databases. J Comput Aided Mol Des 2000; 14: 251-64.
150. Asadabadi EB, Abdolmaleki P, Barkooie SM, Jahandideh S, Rezaei MA. A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase. Comput Biol Med 2009; 39: 1089-95.
151. Munoz-Ruiz P, Rubio L, Garcia-Palomero E, et al. Design, synthesis, and biological evaluation of dual binding site acetylcho-linesterase inhibitors: new disease-modifying agents for Alzheimer's disease. J Med Chem 2005; 48: 7223-33.
152. Van Waterbeemd H. In: Manhnhold R, Krogsgaard-Larsen P,Timmerman H, Van Waterbeemd H, Eds. Chemometric methods in molecular design. New York: Wiley-VCH 1995; Vol. 2, pp. 283-93.
153. Hilbe JM. Logistic regression models. USA: Chapman & Hall/CRC Press 2009.
154. Devillers J. A new strategy for using supervised artificial neural networks in QSAR. SAR QSAR Environ Res 2005; 16: 433-42.
155. Mosier PD, Jurs PC. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J Chem Inf Comput Sci 2002; 42: 1460-70.
156. Kovesdi I, Dominguez-Rodriguez MF, Orfi L, et al. Application of neural networks in structure-activity relationships. Med Res Rev 1999; 19: 249-69.
157. Weinstock M, Razin M, Ringel I, Tashma Z, Chorev M. In: Shafferman A, Velan B, Eds. Acetylcholinesterase inhibition by novel carbamates: a kinetic and nmr study. New York, NY: Plenum 1992; pp. 251-59.
158. Weinstock M, Razin M, Chorev M, Tashma Z. Pharmacological activity of novel anticholinesterase agents of potential use in treatment of Alzheimer's disease. J Neural Transm 1994; 43: 219-25.
159. Lieske CN, Gepp RT, Clark JH, Meyer HG, Blumberg P, Tseng CC. Anticholinesterase activity of potential therapeutic 5-(1,3,3-Trimethylindolinyl) carbamates. J Enzyme Inhib 1991; 5: 215-23.
160. Sharma S, Sahu K, Jain P, Mourya VK, Agrawal RK. QSAR study of 1,3-dioxo-4-methyl-2,3-dihydro-1hpyrrolo [3,4-c]quinolines as caspase-3 inhibitors. Med Chem Res 2008; 17: 399-411.
161. Kravchenko DV, Kysil VV, Ilyn AP., et al. 1,3-Dioxo-4-methyl-2,3-dihydro-1H-pyrrolo [3,4-c]quinolines as potent caspase-3 inhibitors. Bioorg Med Chem Lett 2005; 15: 1841-45.
162. Geldenhuys WJ, Van der Schyf CJ. Serotonin 5-HT6 receptor antagonists for the treatment of alzheimer's disease. Curr Top Med Chem 2008; 8: 1035-48.
163. Ye Y, Liao Q, Wei J, Gao Q. 3D-QSAR study of corticotropinreleasing factor 1 antagonists and pharmacophore-based drug design. Neurochem Int 2010; 56: 107-17.
164. Koo KA, Kim ND, Chon YS, et al. QSAR analysis of pyrazolidine-3,5-diones derivatives as dyrk1A inhibitors. Bioorg Med Chem Lett 2009; 19: 2324-8.
165. Dastmalchi S, Hamzeh-Mivehroud M, Ghafourian T, Hamzeiy H. Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists. J Mol Graph Model 2008; 26: 834-44.
166. Ye M, Dawson MI. 3D-QSAR and QSSR studies of 3,8-diazabicyclo [4.2.0]octane derivatives as neuronal nicotinic acetyl-choline receptors by comparative molecular field analysis (CoMFA). Bioorg Med Chem Lett 2009; 19: 127-31.
167. Ai C, Wang Y, Li Y, Li Y, Yang L. A 3-D QSAR Study of Catechol-O-Methyltransferase Inhibitors Using CoMFA and CoM-SIA. QSAR Comb Sci 2008; 27: 1409-16.
168. Oloff S, Mailman RB, Tropsha A. Application of validated QSAR models of d(1) dopaminergic antagonists for database mining. J Med Chem 2005; 48: 7322-32.
169. Abildstrom SZ, Rasmussen S, Rosen M, Madsen M. Trends in incidence and case fatality rates of acute myocardial infarction in denmark and sweden. Heart 2003; 89: 507-11.
170. Jemal A, Ward E, Hao Y, Thun M. Trends in leading causes of death in the United States, 1970-2002. JAMA 2005; 294: 1255-59.
171. Ong HT. The statin studies: from targeting hypercholesterolemia to targeting the high-risk patient. Q J Med 2005; 98: 599-614.
172. Shah PK. Inhibition of CETP as a novel therapeutic strategy for reducing the risk of atherosclerotic disease. Eur Heart J 2007; 28: 5-12.
173. Barter PJ, Kastelein JJ. Targeting cholesteryl ester transfer protein for the prevention and management of cardiovascular disease. J Am Coll Cardiol 2006; 47: 492-9.
174. Milani RV, Lavie CJ. Cholesteryl ester transfer protein inhibition: the next frontier in combating coronary artery disease? J Am Coll Cardiol 2006; 48: 1791-2.
175. Castilho MS, Guido RV, Andricopulo AD. 2D Quantitative structure-activity relationship studies on a series of cholesteryl ester transfer protein inhibitors. Bioorg Med Chem 2007; 15: 6242-52.
176. Oliveira DB, Gaudio AC. BuildQSAR: a new computer program for QSAR analysis. Quant Struct-Activ Relat 2003; 19: 599-601.
177. Van Aken H, Anger C, Puchstein C, Thijs J, Lawin P. Influence of ketanserin, an antihypertensive agent with specific 5-HT2-receptor blocking activity, on intracranial pressure. Crit Care Med 1984; 12: 4-7.
178. Banes A, Florian JA, Watts SW. Mechanisms of 5-hydroxy-tryptamine2A receptor activation of the mitogen activated protein kinase pathway in vascular smooth muscle. J Pharmacol Exp Ther 1999; 291: 1179-87.
179. MacLean MR, Sweeney G, Baird M, McCulloch KM, Houslay M, Morecroft I. 5-Hydroxytryptamine receptors mediating vasoconstriction in pulmonary arteries from control and pulmonary hypertensive rats. Br J Pharmacol 1996; 119: 917-30.
180. Rao J, Srimal RC, Audry E, Carpy A, Saxena AK. Synthesis and molecular lipophilicity potential profiles of 1-((3-methyl-phenyl)piperazin-1-yl)-3-(thio(4-acetamido)phenyl)-propane. A potential hypotensive agent. Med Chem Res 1991; 1: 95-100.
181. Murti A, Bhandari K, Ram S, et al. Synthesis and QSAR of 1-aryl-4-(beta.-2-quinolyl/1-isoquinolylethyl)piperazines and some related compounds as hypotensive agents. Indian J Chem 1989; 28: 934-42.
182. Saxena AK, Rao J, Chakrabarty R, Saxena M, Srimal RC. Synthesis and QSAR studies on hypotensive 1-[3-(4-substituted phenylthio) propyl]-4-(substituted phenyl) piperazines. Bioorg Med Chem Lett 2007; 17: 1708-12.
183. Opie LH, Frishman WH, Thadani U. In: Opie LH, Ed. Calcium channel Antagonists (Calcium Entry Blockers). Philadelphia: WB Saunders 1994; Vol. 50.
184. Flatters SJL. T-type calcium channels: a potential target for the treatment of chronic pain. Drugs Fut 2005; 30: 573.
185. Van der Vring J, Cleophas T, Van der Wall E, Niemeyer M. T-channel-selective calcium channel blockade: a promising therapeutic possibility, only preliminarily tested so far: a review of published data. Am J Ther 1999; 6: 229-33.
186. Asirvatham S, Sebastian C, Thadani U. Choosing the most appropriate treatment for stable angina. Safety considerations. Drug Saf 1998; 19: 23-44.
187. Seo HN, Choi JY, Choe YJ, et al. Discovery of potent T-type calcium channel blocker. Bioorg Med Chem Lett 2007; 17: 5740-3.
188. Jeong JA, Cho H, Jung SY., et al. 3D QSAR studies on 3,4-dihydroquinazolines as T-type calcium channel blocker by comparative molecular similarity indices analysis (CoMSIA). Bioorg Med Chem Lett 2010; 20: 38-41.
189. Fozard JR, Manley PW. Potassium channel openers. Prog Respir Res 2001; 31: 77-80.
190. Tripathi KD. Essentials of medical pharmacology. New Delhi: Jaypee Brothers Medical Publishers Ltd. 2003.
191. Alam SM, Samanta S, Halder AK, Basu S, Jha T. QSAR modelling of pancreatic beta-cell KATP channel openers R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans using MLR-FA techniques. Eur J Med Chem 2009; 44: 359-64.
192. Snedecor GW, Cochran WG. Statistical methods. New Delhi: Oxford & IBH 1967.
193. Peach MJ. Renin-angiotensin system: biochemistry and mechanisms of action. Physiol Rev 1977; 57: 313-70.
194. Dzau VJ, Pratt RE. In: Haber E, Morgan H, Katz A, Fozard H, Eds. NewYork: Raven 1986; D1631.
195. Zhou YP, Cai CB, Huan S, et al. QSAR study of angiotensin II antagonists using robust boosting partial least squares regression. Anal Chim Acta 2007; 593: 68-74.
196. Peters-Golden M, Henderson WR. Leukotrienes. N Engl J Med 2007; 357: 1841-54.
197. Lewis RA, Austen KF, Soberman RJ. Leukotrienes and other products of the 5-lipoxygenase pathway. N Engl J Med 1990; 323: 645-55.
198. Funk CD. Leukotriene modifiers as potential therapeutics for cardiovascular disease. Nat Rev Drug Discov 2005; 4: 664-72.
199. Young RN. Inhibitors of 5-lipoxygenase: a therapeutic potential yet to be fully realized? Eur J Med Chem 1999; 34: 671-85.
200. Ma X, Zhou L, Zuo ZL, Liu J, Yang M, Wang RW. Molecular docking and 3-D QSAR studies of substituted 2,2-bisaryl-bicyclo-heptanes as human 5-lipoxygenase-activating protein (FLAP) inhibitors. QSAR Combin Sci 2008; 27: 1083-91.
201. Hasegawa K, Miyashita Y, Sasaki S-I. Quantitative structure- activity relationship study of antiarrhythmic phenylpyridines using multivariate partial least squares modelling. Chemomet Intell Lab Syst 1992; 16: 69-75.
202. Fung M, Thornton A, Mybeck K, Wu JH, Hornbuckle K, Muniz E. Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets -1960 to 1999. Drug Inform J 2001; 25: 293-317.
203. Combs AB, Ramos K, Acosta D. In: Hodgen E, Smart RC, Ed. Cardiovascular toxicity. New York: Wiley Interscience 2001; pp. 673-96.
204. Combs AB, Acosta D. Computational toxicology. Risk assessment for pharmaceuticals and environmental chemicals. In: Ekins S, Ed. New York: Wiley Interscience 2006; pp. 3-20.
205. Matthews EJ, Frid AA. Prediction of drug-related cardiac adverse effects in humans-A: Creation of a database of effects and identification of factors affecting their occurrence. Regul Toxicol Pharmacol 2010; 56: 247-75.
206. Welsh JB, Sapinoso LM, Su AI, et al. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 2001; 61: 5974-8.
207. Rivera MP, Stover DE. Gender and lung cancer. Clin Chest Med 2004; 25: 391-400.
208. Kirkegaard T, McGlynn LM, Campbell FM, et al. Amplified in breast cancer 1 in human epidermal growth factor receptor positive tumors of tamoxifen-treated breast cancer patients. Clin Cancer Res 2007; 13: 1405-11.
209. Jain KK. Recent advances in clinical oncoproteomics. J Buon 2007; 12 (Suppl 1): S31-8.
210. Vendemiale G, Grattagliano I, Altomare E. An update on the role of free radicals and antioxidant defense in human disease. Int J Clin Lab Res 1999; 29: 49-55.
211. Cooke MS, Evans MD, Dizdaroglu M, Lunec J. Oxidative DNA damage: mechanisms, mutation, and disease. FASEB J 2003; 17: 1195-214.
212. Halliwell B. Drug antioxidant effects. A basis for drug selection? Drugs 1991; 42: 569-605.
213. Golino P, Ragni M, Cirillo P, et al. Effects of tissue factor induced by oxygen free radicals on coronary flow during reperfusion. Nat Med 1996; 2: 35-40.
214. Esteves MA, Narender N, Marcelo-Curto MJ, Gigante B. Synthetic derivatives of abietic acid with radical scavenging activity. J Nat Prod 2001; 64: 761-6.
215. Ferreira IC, Queiroz MJ, Vilas-Boas M, Estevinho LM, Begouin A, Kirsch G. Evaluation of the antioxidant properties of diarylamines in the benzo [b]thiophene series by free radical scavenging activity and reducing power. Bioorg Med Chem Lett 2006; 16: 1384-7.
216. Queiroz MJ, Ferreira IC, Calhelha RC, Estevinho LM. Synthesis and antioxidant activity evaluation of new 7-aryl or 7-hetero-arylamino-2,3-dimethylbenzo [b]thiophenes obtained by Buchwald-Hartwig C-N cross-coupling. Bioorg Med Chem 2007; 15: 1788-94.
217. Abreu RM, Ferreira IC, Queiroz MJ. QSAR model for predicting radical scavenging activity of di(hetero)arylamines derivatives of benzo [b]thiophenes. Eur J Med Chem 2009; 44: 1952-8.
218. Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intel Lab Syst 2001; 58: 109-30.
219. Bos JL. Ras oncogenes in human cancer: a review. Cancer Res 1989; 49: 4682-9.
220. Gibbs JB. Ras C-terminal processing enzymes--new drug targets? Cell 1991; 65: 1-4.
221. Oliff A. Farnesyltransferase inhibitors: targeting the molecular basis of cancer. Biochim Biophys Acta 1999; 1423: C19-30.
222. Puntambekar DS, Giridhar R, Yadav MR. Insights into the structural requirements of farnesyltransferase inhibitors as potential anti-tumor agents based on 3D-QSAR CoMFA and CoMSIA models. Eur J Med Chem 2008; 43: 142-54.
223. Gonzales-Diaz H, Gia O, Uriarte E, et al. Markovian chemicals in silico design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds. J Mol Model 2003; 9: 395-407.
224. Markov AA. Extension of the law of large numbers to dependent events. Bull Soc Phys Math Kazan (2) 1906; 15: 155-56.
225. Graepel T, Obermayer K. A stochastic self-organizing map for proximity data. Neural Comput 1999 11: 139-55.
226. Geng J, Xu D, Gong J, Li W. Assessing hepatitis A virus epidemic stochastic process in eight cities in china in 1990. Int J Epidemiol 1998; 27: 320-22.
227. Yakovlev A, Boucher K, DiSario J. Modeling insight into spontaneous regression of tumors. Math Biosci 1999; 155: 45-60.
228. Lu Y, Lu S, Fotouhi F, Deng Y, Brown SJ. Incremental genetic K-means algorithm and its application in gene expression data analysis. BMC Bioinformatics 2004; 5: 172.
229. Friedman JH. Regularized Discriminant Analysis. J Am Stat Assoc 1989; 84: 165-75.
230. Ellis J. Proteins as molecular chaperones. Nature 1987; 328: 378-9.
231. Scheibel T, Buchner J. The Hsp90 complex--a super-chaperone machine as a novel drug target. Biochem Pharmacol 1998; 56: 675-82.
232. Smith DF, Whitesell L, Katsanis E. Molecular chaperones. Biology and prospects for pharmacological intervention. Pharmacol Rev 1998; 50: 493-513.
233. Chen B, Piel WH, Gui L, Bruford E, Monteiro A. The HSP90 family of genes in the human genome: insights into their divergence and evolution. Genomics 2005; 86: 627-37.
234. Chiosis G, Vilenchik M, Kim J, Solit D. Hsp90: the vulnerable chaperone. Drug Discov Today 2004; 9: 881-8.
235. Sreedhar AS, Kalmar E, Csermely P, Shen YF. Hsp90 isoforms: functions, expression and clinical importance. FEBS Lett 2004; 562: 11-5.
236. Sakkiah S, Thangapandian S, John S, Kwon YJ, Lee KW. 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. Eur J Med Chem 2010; 45: 2132-40.
237. Huse M, Muir TW, Xu L, Chen YG, Kuriyan J, Massague J. The TGF beta receptor activation process: an inhibitor-to substratebinding switch. Mol Cell 2001; 8: 671-82.
238. Chamberlain J. Transforming growth factor-beta: a promising target for anti-stenosis therapy. Cardiovasc Drug Rev 2001; 19: 329-44.
239. Derynck R, Akhurst RJ, Balmain A. TGF-beta signaling in tumor suppression and cancer progression. Nat Genet 2001; 29: 117-29.
240. Yang M, Zhou L, Zuo Z, et al. Docking study and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses of transforming growth factor-type I receptor kinase inhibitors. QSAR Comb Sci 2009; 28: 1300-08.
241. Allikmets R, Schriml LM, Hutchinson A, Romano-Spica V, Dean M. A human placenta-specific ATP-binding cassette gene (ABCP) on chromosome 4q22 that is involved in multidrug resistance. Cancer Res 1998; 58: 5337-9.
242. Doyle LA, Yang W, Abruzzo LV, et al. A multidrug resistance transporter from human MCF-7 breast cancer cells. Proc Natl Acad Sci USA 1998; 95: 15665-70.
243. Miyake K, Mickley L, Litman T, et al. Molecular cloning of cDNAs which are highly overexpressed in mitoxantrone-resistant cells: demonstration of homology to ABC transport genes. Cancer Res 1999; 59: 8-13.
244. Doyle LA, Ross DD. Multidrug resistance mediated by the breast cancer resistance protein BCRP (ABCG2). Oncogene 2003; 22: 7340-58.
245. Polgar O, Robey RW, Bates SE. ABCG2: structure, function and role in drug response. Expert Opin Drug Metab Toxicol 2008; 4: 1-15.
246. Allen JD, Brinkhuis RF, Wijnholds J, Schinkel AH. The mouse Bcrp1/Mxr/Abcp gene: amplification and overexpression in cell lines selected for resistance to topotecan, mitoxantrone, or doxorubicin. Cancer Res 1999; 59: 4237-41.
247. Schellens JH, Maliepaard M, Scheper RJ, et al. Transport of topoisomerase I inhibitors by the breast cancer resistance protein. Potential clinical implications. Ann N Y Acad Sci 2000; 922: 188-94.
248. Kawabata S, Oka M, Shiozawa K, et al. Breast cancer resistance protein directly confers SN-38 resistance of lung cancer cells. Biochem Biophys Res Commun 2001; 280: 1216-23.
249. Volk EL, Farley KM, Wu Y, Li F, Robey RW, Schneider E. Over-expression of wild-type breast cancer resistance protein mediates methotrexate resistance. Cancer Res 2002; 62: 5035-40.
250. Allen JD, Van Dort SC, Buitelaar M, van Tellingen O, Schinkel AH. Mouse breast cancer resistance protein (Bcrp1/Abcg2) mediates etoposide resistance and transport, but etoposide oral availability is limited primarily by P-glycoprotein. Cancer Res 2003; 63: 1339-44.
251. Burger H, van Tol H, Boersma AW, et al. Imatinib mesylate (STI571) is a substrate for the breast cancer resistance protein (BCRP)/ABCG2 drug pump. Blood 2004; 104: 2940-42.
252. Elkind NB, Szentpetery Z, Apati A, et al. Multidrug transporter ABCG2 prevents tumor cell death induced by the epidermal growth factor receptor inhibitor Iressa (ZD1839, Gefitinib). Cancer Res 2005; 65: 1770-7.
253. Brendel C, Scharenberg C, Dohse M, et al. Imatinib mesylate and nilotinib (AMN107) exhibit high-affinity interaction with ABCG2 on primitive hematopoietic stem cells. Leukemia 2007; 21: 1267-75.
254. Robey RW, Medina-Perez WY, Nishiyama K, et al. Overexpression of the ATP-binding cassette half-transporter, ABCG2 (Mxr/BCrp/ABCP1), in flavopiridol-resistant human breast cancer cells. Clin Cancer Res 2001; 7: 145-52.
255. Komatani H, Kotani H, Hara Y, et al. Identification of breast cancer resistant protein/mitoxantrone resistance/placenta-specific, ATP-binding cassette transporter as a transporter of NB-506 and J-107088, topoisomerase I inhibitors with an indolocarbazole structure. Cancer Res 2001; 61: 2827-32.
256. Nicolle E, Boumendjel A, Macalou S, et al. QSAR analysis and molecular modeling of ABCG2-specific inhibitors. Adv Drug Deliv Rev 2009; 61: 34-46.
257. Kumar N, Shibata D, Helm J, Coppola D, Malafa M. Green tea polyphenols in the prevention of colon cancer. Front Biosci 2007; 12: 2309-15.
258. Mano MS, Duhoux F. Colon cancer: update on adjuvant therapy. Clin Colorectal Cancer 2008; 7: 178-83.
259. Verma RP, Hansch C. Taxane analogues against breast cancer: a quantitative structure-activity relationship study. ChemMedChem 2008; 3: 642-52.
260. Roh EJ, Kim D, Lee CO, Choi SU, Song CE. Structure-activity relationship study at the 3'-N-position of paclitaxel: synthesis and biological evaluation of 3'-N-acyl-paclitaxel analogues. Bioorg Med Chem 2002; 10: 3145-51.
261. Ojima I, Slater JS, Kuduk SD, et al. Synthesis and structure-activity relationships of taxoids derived from 14-hydroxy-10-deacetylbaccatin III. J Med Chem 1997; 40: 267-78.
262. Schiff PB, Fant J, Horwitz SB. Promotion of microtubule assembly in vitro by taxol. Nature 1979; 277: 665-7.
263. Hansch C, Verma RP. Understanding tubulin/microtubule-taxane interactions: a quantitative structure-activity relationship study. Mol Pharm 2008; 5: 151-61.
264. Verma RP, Hansch C. QSAR modeling of taxane analogues against colon cancer. Eur J Med Chem 2010; 45: 1470-7.