Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites

Frontiers in Pharmacology

Volumn 6, Issue MAY, 2015, Pages

  O'Hagan, Steve ^a   Kell, Douglas B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Cheminformatics; Drug transporters; Encodings; Endogenites; Metabolomics

Indexed keywords

DRUG METABOLITE;

ARTICLE; CHEMICAL STRUCTURE; CLUSTER ANALYSIS; DRUG MARKETING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS;

EID: 84930617065     PISSN: None     EISSN: 16639812     Source Type: Journal    
DOI: 10.3389/fphar.2015.00105     Document Type: Article

References (101)

1. Abad-Zapatero, C., Champness, E. J., and Segall, M. D. (2014). Alternative variables in drug discovery: promises and challenges. Future Med. Chem. 6, 577-593. doi: 10.4155/fmc.14.16.
2. Abad-Zapatero, C., Perisic, O., Wass, J., Bento, A. P., Overington, J., Al-Lazikani, B., et al. (2010). Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation. Drug Discov. Today 15, 804-811. doi: 10.1016/j.drudis.2010.08.004.
3. Aldeghi, M., Malhotra, S., Selwood, D. L., and Chan, A. W. E. (2014). Two-and three-dimensional rings in drugs. Chem. Biol. Drug Des. 83, 450-461. doi: 10.1111/cbdd.12260.
4. Al Khalifa, A., Haranczyk, M., and Holliday, J. (2009). Comparison of nonbinary similarity coefficients for similarity searching, clustering and compound selection. J. Chem. Inf. Model. 49, 1193-1201. doi: 10.1021/ci8004644.
5. Beisken, S., Meinl, T., Wiswedel, B., De Figueiredo, L. F., Berthold, M., and Steinbeck, C. (2013). KNIME-CDK: workflow-driven cheminformatics. BMC Bioinformatics 14:257. doi: 10.1186/1471-2105-14-257.
6. Bemis, G. W., and Murcko, M. A. (1996). The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887-2893. doi: 10.1021/jm9602928.
7. Bemis, G. W., and Murcko, M. A. (1999). Properties of known drugs. 2. Side chains. J. Med. Chem. 42, 5095-5099. doi: 10.1021/jm9903996.
8. Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., et al. (2014). The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, D1083-D1090. doi: 10.1093/nar/gkt1031.
9. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., et al. (2008). KNIME: the Konstanz Information Miner. Stud. Class Data Anal. 319, 326. doi: 10.1007/978-3-540-78246-9_38.
10. Bi, J., and Bennett, K. P. (2003). "Regression error characteristic curves, "in Proceedings of 20th International Conference on Machine Learning, eds T. Fawcett and N. Mishra (Washington, DC).
11. Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324.
12. Broadhurst, D., Goodacre, R., Jones, A., Rowland, J. J., and Kell, D. B. (1997). Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal. Chim. Acta 348, 71-86. doi: 10.1016/S0003-2670(97)00065-2.
13. Broadhurst, D., and Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171-196. doi: 10.1007/s11306-006-0037-z.
14. Buzan, T. (2002). How to Mind Map. London: Thorsons.
15. Camp, D., Davis, R. A., Campitelli, M., Ebdon, J., and Quinn, R. J. (2012). Drug-like properties: guiding principles for the design of natural product libraries. J. Nat. Prod. 75, 72-81. doi: 10.1021/np200687v.
16. Chen, B. N., Mueller, C., and Willett, P. (2010). Combination rules for group fusion in similarity-based virtual screening. Mol. Inform. 29, 533-541. doi: 10.1002/minf.201000050.
17. Chen, H. M., Engkvist, O., Blomberg, N., and Li, J. (2012). A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds. MedChemComm 3, 312-321. doi: 10.1039/C2MD00238H.
18. Congreve, M., Carr, R., Murray, C., and Jhoti, H. (2003). A rule of three for fragment-based lead discovery? Drug Discov. Today 8, 876-877. doi: 10.1016/S1359-6446(03)02831-9.
19. Costantino, L., and Barlocco, D. (2006). Privileged structures as leads in medicinal chemistry. Curr. Med. Chem. 13, 65-85. doi: 10.2174/092986706775197999.
20. Davies, D. R., Mamat, B., Magnusson, O. T., Christensen, J., Haraldsson, M. H., Mishra, R., et al. (2009). Discovery of leukotriene A4 hydrolase inhibitors using metabolomics biased fragment crystallography. J. Med. Chem. 52, 4694-4715. doi: 10.1021/jm900259h.
21. Dobson, P. D., and Kell, D. B. (2008). Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat. Rev. Drug Disc. 7, 205-220. doi: 10.1038/nrd2438.
22. Dobson, P. D., Patel, Y., and Kell, D. B. (2009b). "Metabolite-likeness" as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov. Today 14, 31-40. doi: 10.1016/j.drudis.2008.10.011.
23. Dobson, P., Lanthaler, K., Oliver, S. G., and Kell, D. B. (2009a). Implications of the dominant role of cellular transporters in drug uptake. Curr. Top. Med. Chem. 9, 163-184. doi: 10.2174/156802609787521616.
24. Durant, J. L., Leland, B. A., Henry, D. R., and Nourse, J. G. (2002). Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273-1280. doi: 10.1021/ci010132r.
25. Eckert, H., and Bajorath, J. (2007). Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 12, 225-233. doi: 10.1016/j.drudis.2007.01.011.
26. Faulon, J.-L., and Bender, A. (eds.). (2010). Handbook of Chemoinformatics Algorithms. London: CRC Press. doi: 10.1201/9781420082999.
27. Feher, M., and Schmidt, J. M. (2003). Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci. 43, 218-227. doi: 10.1021/ci0200467.
28. Flower, D. R. (1998). On the properties of bit string-based measures of chemical similarity. J. Chem. Inf. Comp. Sci. 38, 379-386. doi: 10.1021/ci970437z.
29. Fontaine, F., Pastor, M., Zamora, I., and Sanz, F. (2005). Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependent descriptors. J. Med. Chem. 48, 2687-2694. doi: 10.1021/jm049113+.
30. Garcia-Sosa, A. T., Maran, U., and Hetényi, C. (2012). Molecular property filters describing pharmacokinetics and drug binding. Curr. Med. Chem. 19, 1646-1662. doi: 10.2174/092986712799945021.
31. Gardiner, E. J., Gillet, V. J., Haranczyk, M., Hert, J. O., Holliday, J. D., Malim, N., et al. (2009). Turbo similarity searching: effect of fingerprint and dataset on virtual-screening performance. Stat. Anal. Data Mining 2, 103-114. doi: 10.1002/sam.10037.
32. Geldenhuys, W. J., Gaasch, K. E., Watson, M., Allen, D. D., and Van Der Schyf, C. J. (2006). Optimizing the use of open-source software applications in drug discovery. Drug Discov. Today 11, 127-132. doi: 10.1016/S1359-6446(05)03692-5.
33. Giacomini, K. M., Huang, S. M., Tweedie, D. J., Benet, L. Z., Brouwer, K. L., Chu, X., et al. (2010). Membrane transporters in drug development. Nat. Rev. Drug Discov. 9, 215-236. doi: 10.1038/nrd3028.
34. Ginn, C. M. R., Willett, P., and Bradshaw, J. (2000). Combination of molecular similarity measures using data fusion. Perspect. Drug Discov. Des. 20, 1-16. doi: 10.1023/A:1008752200506.
35. Godden, J. W., Xue, L., and Bajorath, J. (2000). Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J. Chem. Inf. Comp. Sci. 40, 163-166. doi: 10.1021/ci990316u.
36. Gopal, P., and Dick, T. (2014). Reactive dirty fragments: implications for tuberculosis drug discovery. Curr. Opin. Microbiol. 21C, 7-12. doi: 10.1016/j.mib.2014.06.015.
37. Gupta, S., and Aires-De-Sousa, J. (2007). Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness. Mol. Divers. 11, 23-36. doi: 10.1007/s11030-006-9054-0.
38. Hall, R. J., Mortenson, P. N., and Murray, C. W. (2014). Efficient exploration of chemical space by fragment-based screening. Prog. Biophys. Mol. Biol. 116, 82-91. doi: 10.1016/j.pbiomolbio.2014.09.007.
39. Hamdalla, M. A., Mandoiu, II, Hill, D. W., Rajasekaran, S., and Grant, D. F. (2013). BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. J. Chem. Inf. Model. 53, 601-612. doi: 10.1021/ci300512q.
40. Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., et al. (2004). Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comp. Sci. 44, 1177-1185. doi: 10.1021/ci034231b.
41. Holliday, J. D., Hu, C. Y., and Willett, P. (2002). Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Comb. Chem. High Throughput Screen 5, 155-166. doi: 10.2174/1386207024607338.
42. Holliday, J. D., Salim, N., Whittle, M., and Willett, P. (2003). Analysis and display of the size dependence of chemical similarity coefficients. J. Chem. Inf. Comp. Sci. 43, 819-828. doi: 10.1021/ci034001x.
43. Ilardi, E. A., Vitaku, E., and Njardarson, J. T. (2014). Data-mining for sulfur and fluorine: an evaluation of pharmaceuticals to reveal opportunities for drug design and discovery. J. Med. Chem. 57, 2832-2842. doi: 10.1021/jm401375q.
44. Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G. (2012). ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52, 1757-1768. doi: 10.1021/ci3001277.
45. Karakoc, E., Sahinalp, S. C., and Cherkasov, A. (2006). Comparative QSAR-and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds. J. Chem. Inf. Model. 46, 2167-2182. doi: 10.1021/ci0601517.
46. Kell, D. B. (2013). Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening, and knowledge of transporters: where drug discovery went wrong and how to fix it. FEBS J. 280, 5957-5980. doi: 10.1111/febs.12268.
47. Kell, D. B. (2015). What would be the observable consequences if phospholipid bilayer diffusion of drugs into cells is negligible? Trends Pharmacol. Sci. 36, 15-21. doi: 10.1016/j.tips.2014.10.005.
48. Kell, D. B., Dobson, P. D., Bilsland, E., and Oliver, S. G. (2013). The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov. Today 18, 218-239. doi: 10.1016/j.drudis.2012.11.008.
49. Kell, D. B., Dobson, P. D., and Oliver, S. G. (2011). Pharmaceutical drug transport: the issues and the implications that it is essentially carrier-mediated only. Drug Discov. Today 16, 704-714. doi: 10.1016/j.drudis.2011.05.010.
50. Kell, D. B., and Goodacre, R. (2014). Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov. Today 19, 171-182. doi: 10.1016/j.drudis.2013.07.014.
51. Kell, D. B., and Lurie-Luke, E. (2015). The virtue of innovation: innovation through the lenses of biological evolution. J. R. Soc. Interface 12, 20141183. doi: 10.1098/rsif.2014.1183.
52. Kell, D. B., and Oliver, S. G. (2014). How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Front. Pharmacol. 5:231. doi: 10.3389/fphar.2014.00231.
53. Kell, D. B., Swainston, N., Pir, P., and Oliver, S. G. (2015). Membrane transporter engineering in industrial biotechnology and whole-cell biocatalysis. Trends Biotechnol. 33, 237-246. doi: 10.1016/j.tibtech.2015.02.001.
54. Khanna, V., and Ranganathan, S. (2009). Physicochemical property space distribution among human metabolites, drugs and toxins. BMC Bioinformatics 10:S10. doi: 10.1186/1471-2105-10-S15-S10.
55. Khanna, V., and Ranganathan, S. (2011). Structural diversity of biologically interesting datasets: a scaffold analysis approach. J. Cheminform. 3, 30. doi: 10.1186/1758-2946-3-30.
56. Knight, C. G., Platt, M., Rowe, W., Wedge, D. C., Khan, F., Day, P., et al. (2009). Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape. Nucleic Acids Res. 37, e6. doi: 10.1093/nar/gkn899.
57. Knuth, D. E. (1986). Efficient balanced codes. IEEE Trans. Inf. Theory 32, 51-53. doi: 10.1109/TIT.1986.1057136.
58. Kohonen, T. (1989). Self-Organization and Associative Memory. Berlin: Springer-Verlag. doi: 10.1007/978-3-642-88163-3.
59. Kohonen, T. (2000). Self-organising Maps. Berlin: Springer.
60. Koutsoukas, A., Paricharak, S., Galloway, W. R., Spring, D. R., Ijzerman, A. P., Glen, R. C., et al. (2014). How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. J. Chem. Inf. Model. 54, 230-242. doi: 10.1021/ci400469u.
61. Landrum, G., Lewis, R., Palmer, A., Stiefl, N., and Vulpetti, A. (2011). Making sure there's a "give" associated with the "take": producing and using open-source software in big pharma. J. Cheminform. 3, O3. doi: 10.1186/1758-2946-3-S1-O3.
62. Leach, A. R., and Gillet, V. J. (2007). An Introduction to Chemoinformatics, Revised Edn. Dordrecht: Springer. doi: 10.1007/978-1-4020-6291-9.
63. Lipinski, C. A. (2004). Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol. 1, 337-341. doi: 10.1016/j.ddtec.2004.11.007.
64. Maggiora, G., Vogt, M., Stumpfe, D., and Bajorath, J. (2014). Molecular similarity in medicinal chemistry. J. Med. Chem. 57, 3186-3204. doi: 10.1021/jm401411z.
65. Manallack, D. T., Dennis, M. L., Kelly, M. R., Prankerd, R. J., Yuriev, E., and Chalmers, D. K. (2013). The acid/base profile of the human metabolome and natural products. Mol. Inform. 32, 505-515. doi: 10.1002/minf.201200167.
66. Mazanetz, M. P., Marmon, R. J., Reisser, C. B. T., and Morao, I. (2012). Drug discovery applications for KNIME: an open source data mining platform. Curr. Top. Med. Chem. 12, 1965-1979. doi: 10.2174/156802612804910331.
67. Medina-Franco, J. L., and Maggiora, G. M. (2014). "Molecular similarity analysis, "in Chemoinformatics for Drug Discovery, ed J. Bajorath (Hoboken, NY: Wiley), 343-399.
68. Medina-Franco, J. L., Yongye, A. B., Perez-Villanueva, J., Houghten, R. A., and Martínez-Mayorga, K. (2011). Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. J. Chem. Inf. Model. 51, 2427-2439. doi: 10.1021/ci200281v.
69. Meinl, T., Wiswedel, B., and Berthold, M. (2012). "Workflow tools for managing biological and chemical data, "in Computational Approaches in Chemiformatics and Bioinformatics, eds R. Guha and A. Bender (New York, NY: Wiley), 179-209.
70. Mittas, N., and Angelis, L. (2010). Visual comparison of software cost estimation models by regression error characteristic analysis. J. Syst. Softw. 83, 621-637. doi: 10.1016/j.jss.2009.10.044.
71. Mjos, K. D., and Orvig, C. (2014). Metallodrugs in medicinal inorganic chemistry. Chem. Rev. 114, 4540-4563. doi: 10.1021/cr400460s.
72. Muegge, I. (2003). Selection criteria for drug-like compounds. Med. Res. Rev. 23, 302-321. doi: 10.1002/med.10041.
73. O'Hagan, S., Swainston, N., Handl, J., and Kell, D. B. (2015). A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs. Metabolomics 11, 323-339. doi: 10.1007/s11306-11014-10733-z.
74. Oja, E., and Kaski, S. (eds.). (1998). Kohonen Maps. Amsterdam: Elsevier.
75. Oprea, T. I., Allu, T. K., Fara, D. C., Rad, R. F., Ostopovici, L., and Bologa, C. G. (2007). Lead-like, drug-like or "Pub-like": how different are they? J. Comput. Aided Mol. Des. 21, 113-119. doi: 10.1007/s10822-007-9105-3.
76. Over, B., Wetzel, S., Grütter, C., Nakai, Y., Renner, S., Rauh, D., et al. (2013). Natural-product-derived fragments for fragment-based ligand discovery. Nat. Chem. 5, 21-28. doi: 10.1038/nchem.1506.
77. Peironcely, J. E., Reijmers, T., Coulier, L., Bender, A., and Hankemeier, T. (2011). Understanding and classifying metabolite space and metabolite-likeness. PLoS ONE 6:e28966. doi: 10.1371/journal.pone.0028966.
78. Riniker, S., Fechner, N., and Landrum, G. A. (2013). Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing. J. Chem. Inf. Model. 53, 2829-2836. doi: 10.1021/ci400466r.
79. Ruddigkeit, L., Blum, L. C., and Reymond, J. L. (2013). Visualization and virtual screening of the chemical universe database GDB-17. J. Chem. Inf. Model. 53, 56-65. doi: 10.1021/ci300535x.
80. Ruddigkeit, L., Van Deursen, R., Blum, L. C., and Reymond, J. L. (2012). Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864-2875. doi: 10.1021/ci300415d.
81. Ruusmann, V., Sild, S., and Maran, U. (2014). QSAR DataBank-an approach for the digital organization and archiving of QSAR model information. J. Cheminform. 6, 25. doi: 10.1186/1758-2946-6-25.
82. Sahoo, S., Aurich, M. K., Jonsson, J. J., and Thiele, I. (2014). Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Front. Physiol. 5:91. doi: 10.3389/fphys.2014.00091.
83. Schnur, D. M., Hermsmeier, M. A., and Tebben, A. J. (2006). Are target-family-privileged substructures truly privileged? J. Med. Chem. 49, 2000-2009. doi: 10.1021/jm0502900.
84. Stålring, J. C., Carlsson, L. A., Almeida, P., and Boyer, S. (2011). AZOrange-High performance open source machine learning for QSAR modeling in a graphical programming environment. J. Cheminform. 3, 28. doi: 10.1186/1758-2946-3-28.
85. Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., and Feuston, B. P. (2003). Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43, 1947-1958. doi: 10.1021/ci034160g.
86. Swainston, N., Mendes, P., and Kell, D. B. (2013). An analysis of a 'community-driven' reconstruction of the human metabolic network. Metabolomics 9, 757-764. doi: 10.1007/s11306-013-0564-3.
87. Taylor, R. D., Maccoss, M., and Lawson, A. D. G. (2014). Rings in drugs. J. Med. Chem. 57, 5845-5859. doi: 10.1021/jm4017625.
88. Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., et al. (2013). A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419-425. doi: 10.1038/nbt.2488.
89. Todeschini, R., and Consonni, V. (2009). Molecular Descriptors for Cheminformatics. Weinheim: WILEY-VCH Verlag GmbH. doi: 10.1002/9783527628766.
90. Tounge, B. A., and Reynolds, C. H. (2004). Defining privileged reagents using subsimilarity comparison. J. Chem. Inf. Comp. Sci. 44, 1810-1815. doi: 10.1021/ci049854j.
91. Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Mol. Informat. 29, 476-488. doi: 10.1002/minf.201000061.
92. Vitaku, E., Smith, D. T., and Njardarson, J. T. (2014). Analysis of the structural diversity, substitution patterns, and frequency of nitrogen heterocycles among U.S. FDA approved pharmaceuticals. J. Med. Chem. 57, 10257-10274. doi: 10.1021/jm501100b.
93. Walters, W. P. (2012). Going further than Lipinski's rule in drug design. Exp. Opin. Drug Disc. 7, 99-107. doi: 10.1517/17460441.2012.648612.
94. Wang, Y. A., Eckert, H., and Bajorath, J. (2007). Apparent asymmetry in fingerprint similarity searching is a direct consequence of differences in bit densities and molecular size. ChemMedChem 2, 1037-1042. doi: 10.1002/cmdc.200700050.
95. Warr, W. A. (2011). Some trends in Chem(o)informatics. Meth. Mol. Biol. 672, 1-37. doi: 10.1007/978-1-60761-839-3_1.
96. Whittle, M., Gillet, V. J., Willett, P., and Loesel, J. (2006). Analysis of data fusion methods in virtual screening: theoretical model. J. Chem. Inf. Model. 46, 2193-2205. doi: 10.1021/ci049615w.
97. Willett, P. (2013a). Combination of similarity rankings using data fusion. J. Chem. Inf. Model. 53, 1-10. doi: 10.1021/ci300547g.
98. Willett, P. (2013b). Fusing similarity rankings in ligand-based virtual screening. Comput. Struct. Biotechnol. J. 5: e201302002. doi: 10.5936/csbj.201302002.
99. Wold, S., Sjöström, M., and Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab Syst. 58, 109-130. doi: 10.1016/S0169-7439(01)00155-1.
100. Yusof, I., and Segall, M. D. (2013). Considering the impact drug-like properties have on the chance of success. Drug Discov. Today 18, 659-666. doi: 10.1016/j.drudis.2013.02.008.
101. Zhang, J., Lushington, G. H., and Huan, J. (2011). Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. J. Chem. Inf. Model. 51, 1205-1215. doi: 10.1021/ci1003015.