'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries

Drug Discovery Today

Volumn 14, Issue 1-2, 2009, Pages 31-40

  Dobson, Paul D ^a   Patel, Yogendra ^a   Kell, Douglas B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ATORVASTATIN; DESFLURANE; DISULFIRAM; FLUROTHYL; GANCICLOVIR; METHENAMINE; METHOXYFLURANE; MEVALONIC ACID; ORGANIC ANION TRANSPORTER; PEPTIDE TRANSPORTER 1; PEPTIDE TRANSPORTER 2; SULFUR HEXAFLUORIDE; TENOXICAM; VALGANCICLOVIR;

BAYES THEOREM; BLOOD BRAIN BARRIER; COMBINATORIAL CHEMISTRY; DRUG ABSORPTION; DRUG BIOAVAILABILITY; DRUG DEVELOPMENT; DRUG DISPOSITION; DRUG DISTRIBUTION; DRUG METABOLISM; DRUG PENETRATION; DRUG SOLUBILITY; DRUG TRANSPORT; ENZYME INHIBITION; ESCHERICHIA COLI; FUNCTIONAL GENOMICS; GAS CHROMATOGRAPHY; HIGH THROUGHPUT SCREENING; HUMAN; METABOLOMICS; PLEIOTROPY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; SYSTEMS BIOLOGY; TIME OF FLIGHT MASS SPECTROMETRY;

BIOLOGICAL TRANSPORT; DATABASES, FACTUAL; DRUG DESIGN; HUMANS; METABOLOME; PHARMACEUTICAL PREPARATIONS; SMALL MOLECULE LIBRARIES; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 58149128997     PISSN: 13596446     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.drudis.2008.10.011     Document Type: Review

References (98)

1. Lipinski C., and Hopkins A. Navigating chemical space for biology and medicine. Nature 432 (2004) 855-861
2. Paolini G.V., et al. Global mapping of pharmacological space. Nat. Biotechnol. 24 (2006) 805-815
3. Bleicher K.H., et al. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov. 2 (2003) 369-378
4. Oprea T.I., et al. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41 (2001) 1308-1315
5. Leeson P.D., and Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discov. 6 (2007) 881-890
6. Hodgson J. ADMET - turning chemicals into drugs. Nat. Biotechnol. 19 (2001) 722-726
7. Gleeson M.P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 51 (2008) 817-834
8. Lipinski C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44 (2000) 235-249
9. Noble D., et al. Biological simulations in drug discovery. Drug Discov. Today 4 (1999) 10-16
10. Butcher E.C. Can cell systems biology rescue drug discovery?. Nat. Rev. Drug Discov. 4 (2005) 461-467
11. Cho C.R., et al. The application of systems biology to drug discovery. Curr. Opin. Chem. Biol. 10 (2006) 294-302
12. Kell D.B. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today 11 (2006) 1085-1092
13. Kell D.B. The virtual human: towards a global systems biology of multiscale, distributed biochemical network models. IUBMB Life 59 (2007) 689-695
14. Rishton G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 8 (2003) 86-96
15. Hann M.M., and Oprea T.I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol. 8 (2004) 255-263
16. Wunberg T., et al. Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. Drug Discov. Today 11 (2006) 175-180
17. Gillet V.J., et al. Identification of biological activity profiles using substructural analysis and genetic algorithms. J. Chem. Inf. Comput. Sci. 38 (1998) 165-179
18. Wagener M., and van Geerestein V.J. Potential drugs and nondrugs: prediction and identification of important structural features. J. Chem. Inform. Comput. Sci. 40 (2000) 280-292
19. Hopkins A.L., and Groom C.R. The druggable genome. Nat. Rev. Drug Discov. 1 (2002) 727-730
20. Walters W.P., and Murcko M.A. Prediction of 'drug-likeness'. Adv. Drug Deliv. Rev. 54 (2002) 255-271
21. Muegge I. Selection criteria for drug-like compounds. Med. Res. Rev. 23 (2003) 302-321
22. Ajay. Predicting drug-likeness: why and how?. Curr. Top. Med. Chem. 2 (2002) 1273-1286
23. Biswas D., et al. A simple approach for indexing the oral druglikeness of a compound: discriminating druglike compounds from nondruglike ones. J. Chem. Inf. Model 46 (2006) 1394-1401
24. Reichel A. The role of blood-brain barrier studies in the pharmaceutical industry. Curr. Drug Metab. 7 (2006) 183-203
25. Hopkins A.L., et al. Can we rationally design promiscuous drugs?. Curr. Opin. Struct. Biol. 16 (2006) 127-136
26. Hopkins A., et al. Chemical tools for indications discovery. Ann. Rep. Med. Chem. 40 (2005) 339-348
27. Lipinski C.A., et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23 (1997) 3-25
28. Veber D.F., et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45 (2002) 2615-2623
29. Baurin N., et al. Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7 million compounds. J. Chem. Inf. Comput. Sci. 44 (2004) 643-651
30. Hopkins A.L., et al. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9 (2004) 430-431
31. Rees D.C., et al. Fragment-based lead discovery. Nat. Rev. Drug Discov. 3 (2004) 660-672
32. Abad-Zapatero C., and Metz J.T. Ligand efficiency indices as guideposts for drug discovery. Drug Discov. Today 10 (2005) 464-469
33. Reynolds C.H., et al. The role of molecular size in ligand efficiency. Bioorg. Med. Chem. Lett. 17 (2007) 4258-4261
34. Congreve M., et al. A rule of three for fragment-based lead discovery?. Drug Discov. Today 8 (2003) 876-877
35. Ciulli A., and Abell C. Fragment-based approaches to enzyme inhibition. Curr. Opin. Biotechnol. 18 (2007) 489-496
36. Hubbard R.E., et al. Informatics and modeling challenges in fragment-based drug discovery. Curr. Opin. Drug Discov. Dev. 10 (2007) 289-297
37. Feher M., and Schmidt J.M. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci. 43 (2003) 218-227
38. Ganesan A. The impact of natural products upon modern drug discovery. Curr. Opin. Chem. Biol. 12 (2008) 306-317
39. Sai Y., and Tsuji A. Transporter-mediated drug delivery: recent progress and experimental approaches. Drug Discov. Today 9 (2004) 712-720
40. Sai Y. Biochemical and molecular pharmacological aspects of transporters as determinants of drug disposition. Drug Metab. Pharmacokinet. 20 (2005) 91-99
41. Dobson P.D., and Kell D.B. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule?. Nat. Rev. Drug Discov. 7 (2008) 205-220
42. Cheng A.C., et al. Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25 (2007) 71-75
43. Hajduk P.J., et al. Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 48 (2005) 2518-2525
44. Greene L.H., et al. The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution. Nucleic Acids Res. 35 (2007) D291-297 (database issue)
45. Marsden R.L., et al. Exploiting protein structure data to explore the evolution of protein function and biological complexity. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 361 (2006) 425-440
46. Pang Y.P. In silico drug discovery: solving the "target-rich and lead-poor" imbalance using the genome-to-drug-lead paradigm. Clin. Pharmacol. Ther. 81 (2007) 30-34
47. Duarte N.C., et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 1777-1782
48. Ma H., et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3 (2007) 135
49. Feist A.M., and Palsson B.O. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol 26 6 (2008) 659-667
50. Herrgard M.J., et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 26 10 (2008) 1155-1160
51. Ma H., and Goryanin I. Human metabolic network reconstruction and its impact on drug discovery and development. Drug Discov. Today 13 (2008) 402-408
52. Jenkins H., et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat. Biotechnol. 22 (2004) 1601-1606
53. Brooksbank C., et al. The European Bioinformatics Institute's data resources: towards systems biology. Nucleic Acids Res. 33 (2005) D46-53 (database issue)
54. Smith C.A., et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27 (2005) 747-751
55. Kanehisa M., et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34 (2006) D354-357 (database issue)
56. Spasic I., et al. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7 (2006) 281
57. Wheeler D.L., et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 35 (2007) D5-12 (database issue)
58. Wishart D.S., et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35 (2007) D521-526 (database issue)
59. Spasić I., et al. Facilitating the development of controlled vocabularies for metabolomics with text mining. BMC Bioinformatics 9 (2008) S5
60. Nobeli I., et al. A structure-based anatomy of the E. coli metabolome. J. Mol. Biol. 334 (2003) 697-719
61. Nobeli I., and Thornton J.M. A bioinformatician's view of the metabolome. Bioessays 28 (2006) 534-545
62. Ley R.E., et al. Evolution of mammals and their gut microbes. Science 320 (2008) 1647-1651
63. Gupta S., and Aires-de-Sousa J. Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness. Mol. Divers. 11 (2007) 23-36
64. Irwin J.J., and Shoichet B.K. ZINC - a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model 45 (2005) 177-182
65. Karakoc E., et al. Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds. J. Chem. Inf. Model 46 (2006) 2167-2182
66. Fahy E., et al. A comprehensive classification system for lipids. J. Lipid Res. 46 (2005) 839-861
67. Sud M., et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35 (2007) D527-532 (database issue)
68. Jónsdóttir S.O., et al. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates. Bioinformatics 21 (2005) 2145-2160
69. Csizmadia F., et al. Prediction of distribution coefficient from structure. 1. Estimation method. J. Pharm. Sci. 86 (1997) 865-871
70. Zhang M.Q., and Wilkinson B. Drug discovery beyond the 'rule-of-five'. Curr. Opin. Biotechnol. 18 (2007) 478-488
71. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11 (2006) 1046-1053
72. Hert J., et al. Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org. Biomol. Chem. 2 (2004) 3256-3266
73. Rogers D., et al. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 10 (2005) 682-686
74. James C.A., and Weininger D. Daylight Theory Manual (1995), Daylight Chemical Information Systems, Inc.
75. Flower D.R. On the properties of bit string-based measures of chemical similarity. J. Chem. Inf. Comput. Sci. 38 (1998) 379-386
76. Durant J.L., et al. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42 (2002) 1273-1280
77. Hall L.H., and Kier L.B. The E-state as the basis for molecular structure space definition and structure similarity. J. Chem. Inf. Comput. Sci. 40 (2000) 784-791
78. Kier L.B., and Hall L.H. Database organization and searching with E-state indices. SAR QSAR Environ. Res. 12 (2001) 55-74
79. Wierzbicki A.S., et al. The lipid and non-lipid effects of statins. Pharmacol. Ther. 99 (2003) 95-112
80. Liao J.K., and Laufs U. Pleiotropic effects of statins. Annu. Rev. Pharmacol. Toxicol. 45 (2005) 89-118
81. Wang C.Y., et al. Pleiotropic effects of statin therapy: molecular mechanisms and clinical results. Trends Mol. Med. 14 (2008) 37-44
82. Sugawara M., et al. Transport of valganciclovir, a ganciclovir prodrug, via peptide transporters PEPT1 and PEPT2. J. Pharm. Sci. 89 (2000) 781-789
83. Daniel H., and Kottra G. The proton oligopeptide cotransporter family SLC15 in physiology and pharmacology. Pflugers Arch. 447 (2004) 610-618
84. Sievaen E. Exploitation of bile acid transport systems in prodrug design. Molecules 12 (2007) 1859-1889
85. Tolle-Sander S., et al. Increased acyclovir oral bioavailability via a bile acid conjugate. Mol. Pharm. 1 (2004) 40-48
86. O'Hagan S., et al. Closed-loop, multiobjective optimisation of analytical instrumentation: gas-chromatography-time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem. 77 (2005) 290-303
87. Smith C.A., et al. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78 (2006) 779-787
88. O'Hagan S., et al. Closed-loop, multi-objective optimisation of two-dimensional gas chromatography (GC × GC-tof-MS) for serum metabolomics. Anal. Chem. 79 (2007) 464-476
89. Cotter D., et al. LMPD: LIPID MAPS proteome database. Nucleic Acids Res. 34 (2006) D507-510 (database issue)
90. Yetukuri L., et al. Informatics and computational strategies for the study of lipids. Mol. Biosyst. 4 (2008) 121-127
91. Everitt B.S. Cluster Analysis (1993), Edward Arnold
92. Maggiora G.M., and Shanmugasundaram V. Molecular similarity measures. Methods Mol. Biol. 275 (2004) 1-50
93. Ginn C.M.R., et al. Combination of molecular similarity measures using data fusion. Perspect. Drug Discov. Des. 20 (2000) 1-16
94. Holliday J.D., et al. Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Comb. Chem. High Throughput Screen. 5 (2002) 155-166
95. Kell D.B. Metabolomics, modelling and machine learning in systems biology: towards an understanding of the languages of cells. The 2005 Theodor Bücher lecture. FEBS J. 273 (2006) 873-894
96. Hassan M., et al. Cheminformatics analysis and learning in a data pipelining environment. Mol. Divers. 10 (2006) 283-299
97. Frimurer T.M., et al. Improving the odds in discriminating "drug-like" from "non drug-like" compounds. J. Chem. Inf. Comput. Sci. 40 (2000) 1315-1324
98. Kanehisa M., et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res 36 (Database issue) (2008) D480-D484