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BioMed Research International
Volume 2013 (2013), Article ID 863592, 12 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- D. J. Begley, “ABC transporters and the blood-brain barrier,” Current Pharmaceutical Design, vol. 10, no. 12, pp. 1295–1312, 2004.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- L. E. Dodd and M. S. Pepe, “Partial AUC estimation and regression,” Biometrics, vol. 59, no. 3, pp. 614–623, 2003.
- 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.
- 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.
- 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.
- 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.
- L. L. Pesce, J. Papaioannu, and C. E. Metz, “ROC-kit software 2009,” http://metz-roc.uchicago.edu/MetzROC/software.
- 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.
- M. S. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, Oxford, UK, 2004.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.