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BioMed Research International
Volume 2013 (2013), Article ID 863592, 12 pages
http://dx.doi.org/10.1155/2013/863592
Research Article

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

1Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), Argentinean National Council for Scientific and Technical Research (CONICET), CCT La Plata, Buenos Aires, B1900AJI La Plata, Argentina
2Quality Control of Medications, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, B1900AJI La Plata, Argentina
3Biopharmacy, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47 and 115, Buenos Aires, B1900AJI La Plata, Argentina

Received 30 April 2013; Accepted 25 June 2013

Academic Editor: Jielin Sun

Copyright © 2013 Melisa Edith Gantner et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. M. S. Chan, S. Lowes, and B. H. Hirst, “The ABCs of drug transport in intestine and liver: efflux proteins limiting drug absorption and bioavailability,” European Journal of Pharmaceutical Sciences, vol. 21, no. 1, pp. 25–51, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. C. G. Dietrich, A. Geier, and R. P. J. Oude Elferink, “ABC of oral bioavailability: transporters as gatekeepers in the gut,” Gut, vol. 52, no. 12, pp. 1788–1795, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Potschka, “Role of CNS efflux drug transporters in antiepileptic drug delivery: overcoming CNS efflux drug transport,” Advanced Drug Delivery Reviews, vol. 64, pp. 943–952, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. Z.-S. Chen and A. K. Tiwari, “Multidrug resistance proteins (MRPs/ABCCs) in cancer chemotherapy and genetic diseases,” FEBS Journal, vol. 278, no. 18, pp. 3226–3245, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. K. Tiwari, K. Sodani, C.-L. Dai, C. R. Ashby Jr., and Z.-S. Che, “Revisiting the ABCs of multidrug resistance in cancer chemotherapy,” Current Pharmaceutical Biotechnology, vol. 12, no. 4, pp. 570–594, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Bauer, A. M. S. Hartz, J. R. Lucking, X. Yang, G. M. Pollack, and D. S. Miller, “Coordinated nuclear receptor regulation of the efflux transporter, Mrp2, and the phase-II metabolizing enzyme, GSTπ, at the blood-brain barrier,” Journal of Cerebral Blood Flow and Metabolism, vol. 28, no. 6, pp. 1222–1234, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. O. Burk, “Nuclear receptor-mediated regulation of drug transporters,” in Nuclear Receptors in Drug Metabolism, W. Zie, Ed., John Wiley & Sons, New York, NY, USA, 2009.
  8. G. Englund, F. Rorsman, A. Rönnblom et al., “Regional levels of drug transporters along the human intestinal tract: co-expression of ABC and SLC transporters and comparison with Caco-2 cells,” European Journal of Pharmaceutical Sciences, vol. 29, no. 3-4, pp. 269–277, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Hilgendorf, G. Ahlin, A. Seithel, P. Artursson, A.-L. Ungell, and J. Karlsson, “Expression of thirty-six drug transporter genes in human intestine, liver, kidney, and organotypic cell lines,” Drug Metabolism and Disposition, vol. 35, no. 8, pp. 1333–1340, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. T. G. H. A. Tucker, A. M. Milne, S. Fournel-Gigleux, K. S. Fenner, and M. W. H. Coughtrie, “Absolute immunoquantification of the expression of ABC transporters P-glycoprotein, breast cancer resistance protein and multidrug resistance-associated protein 2 in human liver and duodenum,” Biochemical Pharmacology, vol. 83, no. 2, pp. 279–285, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Dauchy, F. Dutheil, R. J. Weaver et al., “ABC transporters, cytochromes P450 and their main transcription factors: expression at the human blood-brain barrier,” Journal of Neurochemistry, vol. 107, no. 6, pp. 1518–1528, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Shawahna, Y. Uchida, X. Declèves et al., “Transcriptomic and quantitative proteomic analysis of transporters and drug metabolizing enzymes in freshly isolated human brain microvessels,” Molecular Pharmaceutics, vol. 8, no. 4, pp. 1332–1341, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Uchida, S. Ohtsuki, Y. Katsukura et al., “Quantitative targeted absolute proteomics of human blood-brain barrier transporters and receptors,” Journal of Neurochemistry, vol. 117, no. 2, pp. 333–345, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. J. F. Deeken and W. Löscher, “The blood-brain barrier and cancer: transporters, treatment, and trojan horses,” Clinical Cancer Research, vol. 13, no. 6, pp. 1663–1674, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Lhommé, F. Joly, J. L. Walker et al., “Phase III study of valspodar (PSC 833) combined with paclitaxel and carboplatin compared with paclitaxel and carboplatin alone in patients with stage IV or suboptimally debulked stage III epithelial ovarian cancer or primary peritoneal cancer,” Journal of Clinical Oncology, vol. 26, no. 16, pp. 2674–2682, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. N. Akhtar, A. Ahad, R. K. Khar et al., “The emerging role of P-glycoprotein inhibitors in drug delivery: a patent review,” Expert Opinion on Therapeutic Patents, vol. 21, no. 4, pp. 561–576, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. A. M. S. Hartz and B. Bauer, “Regulation of ABC transporters at the blood-brain barrier: new targets for CNS therapy,” Molecular Interventions, vol. 10, no. 5, pp. 293–304, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Milane, S. Ganesh, S. Shah, Z.-F. Duan, and M. Amiji, “Multi-modal strategies for overcoming tumor drug resistance: hypoxia, the Warburg effect, stem cells, and multifunctional nanotechnology,” Journal of Controlled Release, vol. 155, no. 2, pp. 237–247, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. D. J. Begley, “ABC transporters and the blood-brain barrier,” Current Pharmaceutical Design, vol. 10, no. 12, pp. 1295–1312, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Ponte-Sucre, M. Padrón-Nieves, and E. Díaz, “ABC transporter blocker and reversal of drug resistance in microorganisms,” in ABC Transporters in Microorganisms. Research, Innovation and Value as Targets against Drug Resistance, A. Ponte-Sucre, Ed., Caister Academic Press, Norfolk, UK, 2009.
  21. E. Hazai, I. Hazai, I. Ragueneau-Majlessi, S. P. Chung, Z. Bikadi, and Q. Mao, “Predicting substrates of the human breast cancer resistance protein using a support vector machine method.,” BMC Bioinformatics, vol. 14, p. 130, 2013. View at Publisher · View at Google Scholar
  22. L. Zhong, C.-Y. Ma, H. Zhang et al., “A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method,” Computers in Biology and Medicine, vol. 41, no. 11, pp. 1006–1013, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. G. F. Ecker, “QSAR studies on ABC transporter—How to deal with polyspecificity,” in Transporters as Drug Carriers, G. F. Ecker and P. Chiba, Eds., Wiley-VCH, Weinheim, Germany, 2009.
  24. M. A. Demel, O. Krämer, P. Ettmayer, E. E. J. Haaksma, and G. F. Ecker, “Predicting ligand interactions with ABC transporters in ADME,” Chemistry and Biodiversity, vol. 6, no. 11, pp. 1960–1969, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. J. E. Penzotti, M. L. Lamb, E. Evensen, and P. D. J. Grootenhuis, “A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein,” Journal of Medicinal Chemistry, vol. 45, no. 9, pp. 1737–1740, 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. W.-X. Li, L. Li, J. Eksterowicz, X. B. Ling, and M. Cardozo, “Significance analysis and multiple pharmacophore models for differentiating P-glycoprotein substrates,” Journal of Chemical Information and Modeling, vol. 47, no. 6, pp. 2429–2438, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. V. Svetnik, T. Wang, C. Tong, A. Liaw, R. P. Sheridan, and Q. Song, “Boosting: an ensemble learning tool for compound classification and QSAR modeling,” Journal of Chemical Information and Modeling, vol. 45, no. 3, pp. 786–799, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. D.-S. Cao, J.-H. Huang, J. Yan et al., “Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool,” Chemometrics and Intelligent Laboratory Systems, vol. 114, pp. 19–23, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. N. Giri, S. Agarwal, N. Shaik, G. Pan, Y. Chen, and W. F. Elmquist, “Substrate-dependent breast cancer resistance protein (Bcrp1/Abcg2)-mediated interactions: consideration of multiple binding sites in in vitro assay design,” Drug Metabolism and Disposition, vol. 37, no. 3, pp. 560–570, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. K. Takenaka, J. A. Morgan, G. L. Scheffer et al., “Substrate overlap between Mrp4 and Abcg2/Bcrp affects purine analogue drug cytotoxicity and tissue distribution,” Cancer Research, vol. 67, no. 14, pp. 6965–6972, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. E. Hazai and Z. Bikádi, “Homology modeling of breast cancer resistance protein (ABCG2),” Journal of Structural Biology, vol. 162, no. 1, pp. 63–74, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. J. D. Allen, S. C. Jackson, and A. H. Schinkel, “A mutation hot spot in the Bcrp1 (Abcg2) multidrug transporter in mouse cell lines selected for doxorubicin resistance,” Cancer Research, vol. 62, no. 8, pp. 2294–2299, 2002. View at Scopus
  33. C. Özvegy-Laczka, G. Köblös, B. Sarkadi, and A. Váradi, “Single amino acid (482) variants of the ABCG2 multidrug transporter: major differences in transport capacity and substrate recognition,” Biochimica et Biophysica Acta, vol. 1668, no. 1, pp. 53–63, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. R. W. Robey, Y. Honjo, K. Morisaki et al., “Mutations at amino-acid 482 in the ABCG2 gene affect substrate and antagonist specificity,” British Journal of Cancer, vol. 89, no. 10, pp. 1971–1978, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. O. Polgar, R. W. Robey, and S. E. Bates, “ABCG2: structure, function and role in drug response,” Expert Opinion on Drug Metabolism and Toxicology, vol. 4, no. 1, pp. 1–5, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Pozza, J. M. Perez-Victoria, A. Sardo, A. Ahmed-Belkacem, and A. Di Pietro, “Purification of breast cancer resistance protein ABCG2 and role of arginine-482,” Cellular and Molecular Life Sciences, vol. 63, no. 16, pp. 1912–1922, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. T. Janvilisri, S. Shahi, H. Venter, L. Balakrishnan, and H. W. Van Veen, “Arginine-482 is not essential for transport of antibiotics, primary bile acids and unconjugated sterols by the human breast cancer resistance protein (ABCG2),” Biochemical Journal, vol. 385, no. 2, pp. 419–426, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. K. F. K. Ejendal, N. K. Diop, L. C. Schweiger, and C. A. Hrycyna, “The nature of amino acid 482 of human ABCG2 affects substrate transport and ATP hydrolysis but not substrate binding,” Protein Science, vol. 15, no. 7, pp. 1597–1607, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. L. Eddabra, T. Wenner, H. El Btaouri et al., “Arginine 482 to glycine mutation in ABCG2/BCRP increases etoposide transport and resistance to the drug in HEK-293 cells,” Oncology Reports, vol. 27, no. 1, pp. 232–237, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Cervenak, H. Andrikovics, C. Özvegy-Laczka et al., “The role of the human ABCG2 multidrug transporter and its variants in cancer therapy and toxicology,” Cancer Letters, vol. 234, no. 1, pp. 62–72, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. R. Hariharan, A. Janakiraman, R. Nilakantan et al., “MultiMCS: a fast algorithm for the maximum common substructure problem on multiple molecules,” Journal of Chemical Information and Modeling, vol. 51, no. 4, pp. 788–806, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. B. S. Everitt, S. Landau, M. Leese, and D. Stahl, “Optimization clustering techniques,” in Cluster Analysis, D. J. Balding, N. A. C. Cressie, G. M. Fitzmaurice et al., Eds., John Wiley & Sons, Chichester, UK, 5th edition, 2011.
  43. A. Varnek and I. Baskin, “Machine learning methods for property prediction in chemoinformatics: quo vadis?” Journal of Chemical Information and Modeling, vol. 52, pp. 1413–1437, 2012.
  44. T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832–844, 1998. View at Publisher · View at Google Scholar · View at Scopus
  45. A. Talevi, C. L. Bellera, M. Di Ianni, P. R. Duchowicz, L. E. Bruno-Blanch, and E. A. Castro, “An integrated drug development approach applying topological descriptors,” Current Computer-Aided Drug Design, vol. 8, no. 3, pp. 172–181, 2012.
  46. N. Triballeau, F. Acher, I. Brabet, J.-P. Pin, and H.-O. Bertrand, “Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4,” Journal of Medicinal Chemistry, vol. 48, no. 7, pp. 2534–2547, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. R. Hubbard and M. J. Bayarri, “Confusion over measures of evidence (p's) versus errors (α's) in classical statistical testing,” American Statistician, vol. 57, no. 3, pp. 171–178, 2003. View at Scopus
  48. J.-F. Truchon and C. I. Bayly, “Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem,” Journal of Chemical Information and Modeling, vol. 47, no. 2, pp. 488–508, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. L. E. Dodd and M. S. Pepe, “Partial AUC estimation and regression,” Biometrics, vol. 59, no. 3, pp. 614–623, 2003. View at Publisher · View at Google Scholar · View at Scopus
  50. J. Kirchmair, P. Markt, S. Distinto, G. Wolber, and T. Langer, “Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—what can we learn from earlier mistakes?” Journal of Computer-Aided Molecular Design, vol. 22, no. 3-4, pp. 213–228, 2008. View at Publisher · View at Google Scholar · View at Scopus
  51. W. Zhao, K. E. Hevener, S. W. White, R. E. Lee, and J. M. Boyett, “A statistical framework to evaluate virtual screening,” BMC Bioinformatics, vol. 10, article 225, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. D. Bamber, “The area above the ordinal dominance graph and the area below the receiver operating characteristic graph,” Journal of Mathematical Psychology, vol. 12, no. 4, pp. 387–415, 1975. View at Scopus
  53. J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology, vol. 143, no. 1, pp. 29–36, 1982. View at Scopus
  54. L. L. Pesce, J. Papaioannu, and C. E. Metz, “ROC-kit software 2009,” http://metz-roc.uchicago.edu/MetzROC/software.
  55. X. Robin, N. Turck, A. Hainard et al., “pROC: an open-source package for R and S+ to analyze and compare ROC curves,” BMC Bioinformatics, vol. 12, article 77, 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. M. S. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, Oxford, UK, 2004.
  57. L. L. Pesce, C. E. Metz, and K. S. Berbaum, “On the convexity of ROC curves estimated from radiological test results,” Academic Radiology, vol. 17, no. 8, pp. 960–968.e4, 2010. View at Publisher · View at Google Scholar · View at Scopus
  58. E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics, vol. 44, no. 3, pp. 837–845, 1988. View at Scopus
  59. J. Carpenter and J. Bithell, “Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians,” Statistics in Medicine, vol. 19, no. 9, pp. 1141–1164, 2000.
  60. V. Kairys, M. X. Fernandes, and M. K. Gilson, “Screening drug-like compounds by docking to homology models: a systematic study,” Journal of Chemical Information and Modeling, vol. 46, no. 1, pp. 365–379, 2006. View at Publisher · View at Google Scholar · View at Scopus
  61. S. K. Kearsley, S. Sallamack, E. M. Fluder, J. D. Andose, R. T. Mosley, and R. P. Sheridan, “Chemical similarity using physiochemical property descriptors,” Journal of Chemical Information and Computer Sciences, vol. 36, no. 1, pp. 118–127, 1996. View at Scopus
  62. R. P. Sheridan, S. B. Singh, E. M. Fluder, and S. K. Kearsley, “Protocols for bridging the peptide to nonpeptide gap in topological similarity searches,” Journal of Chemical Information and Computer Sciences, vol. 41, no. 3–6, pp. 1395–1406, 2001. View at Scopus
  63. H. Yabuuchi, S. Niijima, H. Takematsu et al., “Analysis of multiple compound-protein interactions reveals novel bioactive molecules,” Molecular Systems Biology, vol. 7, article 472, 2011. View at Publisher · View at Google Scholar · View at Scopus
  64. I. V. Tetko, “Neural network studies. 4. Introduction to associative neural networks,” Journal of Chemical Information and Computer Sciences, vol. 42, no. 3, pp. 717–728, 2002. View at Publisher · View at Google Scholar · View at Scopus
  65. I. V. Tetko, V. Y. Tanchuk, N. P. Chentsova et al., “HIV-1 reverse transcriptase inhibitor design using artificial neural networks,” Journal of Medicinal Chemistry, vol. 37, no. 16, pp. 2520–2526, 1994. View at Scopus
  66. N. V. Artemenko, I. I. Baskin, V. A. Palyulin, and N. S. Zefirov, “Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds,” Russian Chemical Bulletin, vol. 52, no. 1, pp. 20–29, 2003. View at Publisher · View at Google Scholar · View at Scopus
  67. N. I. Zhokhova, I. I. Baskin, V. A. Palyulin, A. N. Zefirov, and N. S. Zefirov, “Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies,” Doklady Chemistry, vol. 417, no. 2, pp. 282–284, 2007. View at Publisher · View at Google Scholar · View at Scopus
  68. H. Zhu, A. Tropsha, D. Fourches et al., “Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis,” Journal of Chemical Information and Modeling, vol. 48, no. 4, pp. 766–784, 2008. View at Publisher · View at Google Scholar · View at Scopus
  69. A. Varnek, D. Fourches, D. Horvath et al., “ISIDA: platform for virtual screening based on fragment and pharmacophoric descriptors,” Current Computer-Aided Drug Design, vol. 4, no. 3, pp. 191–198, 2008. View at Publisher · View at Google Scholar · View at Scopus