DOI QR코드

DOI QR Code

Development of kNN QSAR Models for 3-Arylisoquinoline Antitumor Agents

  • Tropsha, Alexander (Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina) ;
  • Golbraikh, Alexander (Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina) ;
  • Cho, Won-Jea (College of Pharmacy and Research Institute of Drug Development, Chonnam National University)
  • Received : 2011.04.04
  • Accepted : 2011.06.01
  • Published : 2011.07.20

Abstract

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.

Keywords

References

  1. Cook, S. J.; Wakelam, M. Curr. Opin. Pharmacol. 2005, 5, 341. https://doi.org/10.1016/j.coph.2005.05.002
  2. Fan, Q. L.; Zou, W. Y.; Song, L. H.; Wei, W. Cancer Chemother. Pharmacol. 2005, 55, 189. https://doi.org/10.1007/s00280-004-0867-1
  3. Inagawa, H.; Nishizawa, T.; Honda, T.; Nakamoto, T.; Takagi, K.; Soma, G. Anticancer Res. 1998, 18, 3957.
  4. Le, T. N.; Gang, S. G.; Cho, W. J. Tetrahedron Lett. 2004, 45, 2763. https://doi.org/10.1016/j.tetlet.2004.02.031
  5. Nakanishi, T.; Masuda, A.; Suwa, M.; Akiyama, Y.; Hoshino-Abe, N.; Suzuki, M. Bioorg. Med. Chem. Lett. 2000, 10, 2321. https://doi.org/10.1016/S0960-894X(00)00467-4
  6. Vogt, A.; Tamewitz, A.; Skoko, J.; Sikorski, R. P.; Giuliano, K. A.; Lazo, J. S. J. Biol. Chem. 2005, 280, 19078. https://doi.org/10.1074/jbc.M501467200
  7. Cho, W. J.; Park, M. J.; Chung, B. H.; Lee, C. O. Bioorg. Med. Chem. Lett. 1998, 8, 41. https://doi.org/10.1016/S0960-894X(97)10190-1
  8. Cho, W. J.; Park, M. J.; Imanishi, T.; Chung, B. H. Chem. Pharm. Bull. 1999, 47, 900. https://doi.org/10.1248/cpb.47.900
  9. Cho, W. J.; Min, S. Y.; Le, T. N.; Kim, T. S. Bioorg. Med. Chem. Lett. 2003, 13, 4451. https://doi.org/10.1016/j.bmcl.2003.09.001
  10. Lee, S. H.; Van, H. T. M.; Yang, S. H.; Lee, K. T.; Kwon, Y.; Cho, W. J. Bioorg. Med. Chem. Lett. 2009, 19, 2444. https://doi.org/10.1016/j.bmcl.2009.03.058
  11. Van, H. T. M.; Le, Q. M.; Lee, K. Y.; Lee, E. S.; Kwon, Y.; Kim, T. S.; Le, T. N.; Lee, S. H.; Cho, W. J. Bioorg. Med. Chem. Lett. 2007, 17, 5763. https://doi.org/10.1016/j.bmcl.2007.08.062
  12. Cho, W. J.; Le, Q. M.; Van, H. T. M.; Lee, K. Y.; Kang, B. Y.; Lee, E. S.; Lee, S. K.; Kwon, Y. Bioorg. Med. Chem. Lett. 2007, 17, 3531. https://doi.org/10.1016/j.bmcl.2007.04.064
  13. Ioanoviciu, A.; Antony, S.; Pommier, Y.; Staker, B. L.; Stewart, L.; Cushman, M. J. Med. Chem. 2005, 48, 4803. https://doi.org/10.1021/jm050076b
  14. Staker, B. L.; Feese, M. D.; Cushman, M.; Pommier, Y.; Zembower, D.; Stewart, L.; Burgin, A. B. J. Med. Chem. 2005, 48, 2336. https://doi.org/10.1021/jm049146p
  15. Xiao, X.; Antony, S.; Pommier, Y.; Cushman, M. J. Med. Chem. 2005, 48, 3231. https://doi.org/10.1021/jm050017y
  16. Cho, W. J.; Kim, E. K.; Park, I. Y.; Jeong, E. Y.; Kim, T. S.; Le, T. N.; Kim, D. D.; Leed, E. S. Bioorg. Med. Chem. 2002, 10, 2953. https://doi.org/10.1016/S0968-0896(02)00137-2
  17. Kim, K. E.; Cho, W. J.; Chang, S. J.; Yong, C. S.; Lee, C. H.; Kim, D. D. Int. J. Pharm. 2001, 217, 101. https://doi.org/10.1016/S0378-5173(01)00593-2
  18. Kim, K. E.; Cho, W. J.; Kim, T. S.; Kang, B. H.; Chang, S. J.; Lee, C. H.; Kim, D. D. Drug. Dev. Ind. Pharm. 2002, 28, 889. https://doi.org/10.1081/DDC-120005634
  19. Cramer III, R. D.; Patterson, D. E.; Bunce, J. D. J. Am. Chem. Soc. 1988, 110, 5959. https://doi.org/10.1021/ja00226a005
  20. Marshall, G. R.; Cramer, R. D. Trends Pharmacol. Sci. 1988, 9, 285. https://doi.org/10.1016/0165-6147(88)90012-0
  21. Cho, S. J.; Tropsha, A. J. Med. Chem. 1995, 38, 1060. https://doi.org/10.1021/jm00007a003
  22. Klebe, G. Comparative Molecular Similarity Indices Analysis- CoMSIA. In 3D QSAR in Drug Design; Kluwer/ESCOM: Dodrecht, 1988.
  23. Kubinyi, H.; Hamprecht, F. A.; Mietzner, T. J. Med. Chem. 1998, 41, 2553. https://doi.org/10.1021/jm970732a
  24. Perez, C.; Pastor, M.; Ortiz, A.; Gago, F. J. Med. Chem. 1988, 41, 836.
  25. Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147. https://doi.org/10.1021/ci000082a
  26. Allen, F. H.; Bellard, S.; Brice, M. D.; Cartwright, B. A.; Doubleday, A.; Higgs, H.; Hummelink, T.; Hummelink-Peters, B. G.; Kennard, O.; Motherwell, S. W. D.; Rodgers, J. R.; Watson, D. G. Acta Crystallogr., Sect B: Struct., Crystallogr. Cryst. Chem. 1979, B 35, 2331.
  27. Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D. J. Comput. Aided Mol. Des. 2001, 15, 411. https://doi.org/10.1023/A:1011115820450
  28. Morris, G. M.; Goodsell, D. S.; Huey, R.; Olson, A. J. J. Comput. Aided Mol. Des. 1996, 4, 293.
  29. Osterberg, F.; Morris, G. M.; Sanner, M. F.; Olson, A. J.; Goodsell, D. S. Proteins 2002, 46, 34. https://doi.org/10.1002/prot.10028
  30. Jones, G.; Willett, P.; Glen, R. C. J. Mol. Biol. 1995, 245, 43. https://doi.org/10.1016/S0022-2836(95)80037-9
  31. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Proteins 2003, 52, 609. https://doi.org/10.1002/prot.10465
  32. Holloway, M. K.; Wai, J. M.; Halgren, T. A.; Fitzgerald, P. M. D.; Vacca, J. P.; Dorsey, B. D.; Levin, R. B.; Thompson, W. J.; Chen, L. J.; deSolms, S. J.; Gaffin, N.; Ghosh, A. K.; Giuliani, E. A.; Graham, S. L.; Guare, J. P.; Hungate, R. W.; Lyle, T. A.; Sanders, W. M.; Tucker, T. J.; Wiggins, M.; Wiscount, C. M.; Woltersdorf, O. W.; Young, S. D.; Darke, P. L.; Zugay, J. A. J. Med. Chem. 1995, 38, 305. https://doi.org/10.1021/jm00002a012
  33. Judson, R. Genetic Algorithms and Their Use in Chemistry. In: Reviews in Computational Chemistry; VCH: 1997.
  34. Kramer, B.; Rarey, M.; Lengauer, T. Proteins 1999, 37, 228. https://doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228::AID-PROT8>3.0.CO;2-8
  35. Claussen, H.; Buning, C.; Rarey, M.; Lengauer, T. J. Mol. Biol. 2001, 27, 377.
  36. Cho, S. J.; Serrano, M. G.; Bier, J.; Tropsha, A. J. Med. Chem. 1996, 39, 5064. https://doi.org/10.1021/jm950771r
  37. Pilger, C.; Bartolucci, C.; Lamba, D.; Tropsha, A.; Fels, G. J. Mol. Graph. Model. 2001, 19, 288. https://doi.org/10.1016/S1093-3263(00)00056-5
  38. Shen, M.; Beguin, C.; Golbraikh, A.; Stables, J. P.; Kohn, H.; Tropsha, A. J. Med. Chem. 2004, 47, 2356. https://doi.org/10.1021/jm030584q
  39. Oloff, S.; Mailman, R. B.; Tropsha, A. J. Med. Chem. 2005, 48, 7322. https://doi.org/10.1021/jm049116m
  40. Shen, M.; LeTiran, A.; Xiao, Y.; Golbraikh, A.; Kohn, H.; Tropsha, A. J. Med. Chem. 2002, 45, 2811. https://doi.org/10.1021/jm010488u
  41. Hoffman, B.; Cho, S. J.; Zheng, W.; Wyrick, S.; Nichols, D. E.; Mailman, R. B.; Tropsha, A. J. Med. Chem. 1999, 42, 3217. https://doi.org/10.1021/jm980415j
  42. Zheng, W.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2000, 40, 185. https://doi.org/10.1021/ci980033m
  43. Cho, S. J.; Zheng, W.; Tropsha, A. J. Chem. Inf. Comput. Sci. 1998, 38, 259. https://doi.org/10.1021/ci9700945
  44. Cho, S. J.; Zheng, W.; Tropsha, A. Pac. Symp. Biocomput. 1998, 305.
  45. Zheng, W.; Cho, S. J.; Tropsha, A. J. Chem. Inf. Comput. Sci. 1998, 38, 251. https://doi.org/10.1021/ci970095x
  46. Rubinstein, L. V.; Shoemaker, R. H.; Paull, K. D.; Simon, R. M.; Tosini, S.; Skehan, P.; Scudiero, D. A.; Monks, A.; Boyd, M. R. J. Natl. Cancer Inst. 1990, 82, 1113. https://doi.org/10.1093/jnci/82.13.1113
  47. Kier, L. B.; Hall, L. H. Molecular Connectivity in Chemistry and Drug Research; Academic Press: New York, 1986.
  48. Kier, L. B. H. Molecular Connectivity in Chemistry and Drug Research; Academic Press: New York, 1976.
  49. Randic, M. J. Am.Chem. Soc. 1975, 97, 6609. https://doi.org/10.1021/ja00856a001
  50. Kier, L. B. Quant. Struct.-Act. Relat. 1985, 4, 109. https://doi.org/10.1002/qsar.19850040303
  51. Kier, L. B. Quant. Struct-Act. Relat. 1987, 6, 8. https://doi.org/10.1002/qsar.19870060103
  52. Hall, L. H.; Kier, L. B. Quant. Struct.-Act. Relat. 1990, 9, 115. https://doi.org/10.1002/qsar.19900090207
  53. Hall, L. H.; Mohney, B. K.; Kier, L. B. J. Chem. Inf. Comput. Sci. 1991, 31, 76. https://doi.org/10.1021/ci00001a012
  54. Hall, L. H.; Mohney, B. K.; Kier, L. B. Quant. Struct.-Act. Relat. 1991, 10, 43. https://doi.org/10.1002/qsar.19910100108
  55. Kellogg, G. E.; Kier, L. B.; Gaillard, P.; Hall, L. H. J. Comput. Aided Mol. Des. 1996, 10, 513. https://doi.org/10.1007/BF00134175
  56. Kier, L. B.; Hall, L. H. Molecular Structure Description: The Electrotopological State; Academic Press: 1999.
  57. Kier, L. B.; Hall, L. H. Quant. Struct.-Act. Relat. 1991, 10, 134. https://doi.org/10.1002/qsar.19910100208
  58. Petitjean, M. J. Chem. Inf. Comput. Sci. 1992, 32, 331. https://doi.org/10.1021/ci00008a012
  59. Wiener, H. J. J. Am. Chem. Soc. 1947, 69, 17. https://doi.org/10.1021/ja01193a005
  60. Platt, J. R. J. Chem. Phys. 1947, 15, 419. https://doi.org/10.1063/1.1746554
  61. Shannon, C.; Weaver, W. In Mathematical Theory of Communication; University of Illinois: Urbana, 1949.
  62. Bonchev, D.; Mekenyan, O.; Trinajstic, N. J. Comput. Chem. 1981, 2, 127. https://doi.org/10.1002/jcc.540020202
  63. Basak, S. C.; Mills, D. SAR QSAR Environ. Res. 2001, 12, 481. https://doi.org/10.1080/10629360108039830
  64. Benigni, R.; Giuliani, A.; Franke, R.; Gruska, A. Chem. Rev. 2000, 100, 3697. https://doi.org/10.1021/cr9901079
  65. Cronin, M. T.; Dearden, J. C.; Duffy, J. C.; Edwards, R.; Manga, N.; Worth, A. P.; Worgan, A. D. SAR QSAR Environ. Res. 2002, 13, 167.
  66. Fan, Y.; Shi, L. M.; Kohn, K. W.; Pommier, Y.; Weinstein, J. N. J. Med. Chem. 2001, 44, 3254. https://doi.org/10.1021/jm0005151
  67. Girones, X.; Gallegos, A.; Carbo-Dorca, R. J. Chem. Inf. Comput. Sci. 2000, 40, 1400. https://doi.org/10.1021/ci0004558
  68. Moss, G. P.; Dearden, J. C.; Patel, H.; Cronin, M. T. Toxicol. In Vitro 2002, 16, 299. https://doi.org/10.1016/S0887-2333(02)00003-6
  69. Randic, M.; Basak, S. C. J. Chem. Inf. Comput. Sci. 2000, 40, 899. https://doi.org/10.1021/ci990115q
  70. Suzuki, T.; Ide, K.; Ishida, M.; Shapiro, S. J. Chem. Inf. Comput. Sci. 2001, 41, 718. https://doi.org/10.1021/ci000333f
  71. Trohalaki, S.; Gifford, E.; Pachter, R. Comput. Chem. 2000, 24, 421. https://doi.org/10.1016/S0097-8485(99)00093-5
  72. Wang, X.; Yin, C.; Wang, L. Chemosphere 2002, 46, 1045. https://doi.org/10.1016/S0045-6535(01)00148-5
  73. Golbraikh, A.; Tropsha, A. J. Mol. Graph. Model. 2002, 20, 269. https://doi.org/10.1016/S1093-3263(01)00123-1
  74. Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y. D.; Lee, K. H.; Tropsha, A. J. Comput. Aided Mol. Des. 2003, 17, 241. https://doi.org/10.1023/A:1025386326946
  75. Golbraikh, A.; Tropsha, A. J. Comput. Aided Mol. Des. 2002, 16, 357. https://doi.org/10.1023/A:1020869118689
  76. Pintore, M.; Piclin, N.; Benfenati, E.; Gini, G.; Chretien, J. R. Qsar & Comb. Sci. 2003, 22, 210. https://doi.org/10.1002/qsar.200390014
  77. Zhang, S.; Golbraikh, A.; Tropsha, A. J. Med. Chem. 2006, 49, 2713. https://doi.org/10.1021/jm050260x
  78. Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2002, 42, 769. https://doi.org/10.1021/ci0103469
  79. Wold, S. a. E. L. Statistical Validation of QSAR Results. In Chemometrics Methods in Molecular Design; VCH: Weinheim, Germany, 1995.
  80. Tropsha, A.; Golbraikh, A. Curr. Pharm. Des. 2007, 13, 3494. https://doi.org/10.2174/138161207782794257

Cited by

  1. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches vol.22, pp.22, 2015, https://doi.org/10.1007/s11356-015-4965-x
  2. Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose vol.24, pp.4, 2015, https://doi.org/10.1007/s10646-015-1431-y
  3. Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes vol.55, pp.7, 2015, https://doi.org/10.1021/acs.jcim.5b00139
  4. -Oxides vol.80, pp.11, 2015, https://doi.org/10.1021/acs.joc.5b00475
  5. Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches vol.23, pp.14, 2016, https://doi.org/10.1007/s11356-016-6527-2
  6. Room-temperature and temperature-dependent QSRR modelling for predicting the nitrate radical reaction rate constants of organic chemicals using ensemble learning methods vol.27, pp.7, 2016, https://doi.org/10.1080/1062936X.2016.1199592
  7. Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches vol.5, pp.1, 2016, https://doi.org/10.1039/C5TX00321K
  8. In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes vol.5, pp.3, 2016, https://doi.org/10.1039/C5TX00493D
  9. QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes vol.5, pp.4, 2016, https://doi.org/10.1039/C6TX00083E
  10. Three-Tier Strategy for Screening High-Energy Molecules Using Structure–Property Relationship Modeling Approaches vol.55, pp.3, 2016, https://doi.org/10.1021/acs.iecr.5b03575
  11. Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015) vol.88, pp.3, 2016, https://doi.org/10.1515/pac-2012-1204
  12. Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach vol.24, pp.32, 2017, https://doi.org/10.1007/s11356-017-0161-5
  13. Predicting aquatic toxicities of benzene derivatives in multiple test species using local, global and interspecies QSTR modeling approaches vol.5, pp.87, 2015, https://doi.org/10.1039/c5ra12825k
  14. Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches. vol.26, pp.6, 2011, https://doi.org/10.1080/1062936x.2015.1051584
  15. Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches vol.27, pp.1, 2011, https://doi.org/10.1080/1062936x.2015.1133700