Advertisement

Molecular Diversity

, Volume 20, Issue 4, pp 945–961 | Cite as

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds

  • Trieu-Du Ngo
  • Thanh-Dao Tran
  • Minh-Tri Le
  • Khac-Minh ThaiEmail author
Original Article

Abstract

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure–activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

Keywords

Machine learning Docking P-glycoprotein Chalcone Drug bank 

Notes

Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number 106-YS.05-2015.31 to Khac-Minh Thai.

Compliance with ethical standards

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Supplementary material

11030_2016_9688_MOESM1_ESM.pdf (988 kb)
Supplementary material 1 (pdf 987 KB)
11030_2016_9688_MOESM2_ESM.xlsx (2.4 mb)
Supplementary material 2 (xlsx 2409 KB)

References

  1. 1.
    Teodori E, Dei S, Martelli C, Scapecchi S, Gualtieri F (2006) The functions and structure of ABC transporters: implications for the design of new inhibitors of Pgp and MRP1 to control multidrug resistance (MDR). Curr Drug Targets 7:893–909. doi: 10.2174/138945006777709520 CrossRefPubMedGoogle Scholar
  2. 2.
    Zhou SF (2008) Structure, function and regulation of P-glycoprotein and its clinical relevance in drug disposition. Xenobiotica 38:802–832. doi: 10.1080/00498250701867889 CrossRefPubMedGoogle Scholar
  3. 3.
    Sharom FJ (2006) Multidrug resistance protein: P-glycoprotein. In: Drug transporters. Wiley, New York, pp 223–262. doi: 10.1002/9780470140505.ch10
  4. 4.
    Glaeser H, Fromm MF, König J (2008) Transporters and drugs–an overview. In: Antitargets. Wiley-VCH Verlag GmbH & Co. KGaA, pp 341–366. doi: 10.1002/9783527621460.ch15
  5. 5.
    Thai KM, Ngo TD, Tran TD, Le MT (2013) Pharmacophore modeling for antitargets. Curr Top Med Chem 13:1002–1014. doi: 10.2174/1568026611313090004 CrossRefPubMedGoogle Scholar
  6. 6.
    Vaz RJ, Klabunde T (2008) A personal foreword. In: Antitargets. Wiley-VCH Verlag GmbH & Co. KGaA, pp XXI–XXIV. doi: 10.1002/9783527621460.fmatter
  7. 7.
    Lehne G (2000) P-glycoprotein as a drug target in the treatment of multidrug resistant cancer. Curr Drug Targets 1:85–99. doi: 10.2174/1389450003349443 CrossRefPubMedGoogle Scholar
  8. 8.
    Binkhathlan Z, Lavasanifar A (2013) P-glycoprotein inhibition as a therapeutic approach for overcoming multidrug resistance in cancer: current status and future perspectives. Curr Cancer Drug Targets 13:326–346. doi: 10.2174/15680096113139990076 CrossRefPubMedGoogle Scholar
  9. 9.
    Ronchi E, Sanfilippo O, Di Fronzo G, Bani MR, Della Torre G, Catania S, Silvestrini R (1989) Detection of the 170 kDa P-glycoprotein in neoplastic and normal tissues. Tumori 75:542–546PubMedGoogle Scholar
  10. 10.
    Aller SG, Yu J, Ward A, Weng Y, Chittaboina S, Zhuo R, Harrell PM, Trinh YT, Zhang Q, Urbatsch IL, Chang G (2009) Structure of P-glycoprotein reveals a molecular basis for poly-specific drug binding. Science 323:1718–1722. doi: 10.1126/science.1168750 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Shintre CA, Pike AC, Li Q, Kim JI, Barr AJ, Goubin S, Shrestha L, Yang J, Berridge G, Ross J, Stansfeld PJ, Sansom MS, Edwards AM, Bountra C, Marsden BD, von Delft F, Bullock AN, Gileadi O, Burgess-Brown NA, Carpenter EP (2013) Structures of ABCB10, a human ATP-binding cassette transporter in apo- and nucleotide-bound states. Proc Natl Acad Sci USA 110:9710–9715. doi: 10.1073/pnas.1217042110 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Montanari F, Ecker GF (2015) Prediction of drug-ABC-transporter interaction—recent advances and future challenges. Adv Drug Deliv Rev 86:17–26. doi: 10.1016/j.addr.2015.03.001 CrossRefPubMedGoogle Scholar
  13. 13.
    Chufan EE, Kapoor K, Sim HM, Singh S, Talele TT, Durell SR, Ambudkar SV (2013) Multiple transport-active binding sites are available for a single substrate on human P-glycoprotein (ABCB1). PloS One 8:e82463. doi: 10.1371/journal.pone.0082463 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Chiba P, Mihalek I, Ecker GF, Kopp S, Lichtarge O (2006) Role of transmembrane domain/transmembrane domain interfaces of P-glycoprotein (ABCB1) in solute transport. Convergent information from photoaffinity labeling, site directed mutagenesis and in silico importance prediction. Curr Med Chem 13:793–805. doi: 10.2174/092986706776055607 CrossRefPubMedGoogle Scholar
  15. 15.
    Palmeira A, Sousa E, Vasconcelos MH, Pinto MM (2012) Three decades of P-gp inhibitors: skimming through several generations and scaffolds. Curr Med Chem 19:1946–2025. doi: 10.2174/092986712800167392 CrossRefPubMedGoogle Scholar
  16. 16.
    Szakacs G, Paterson JK, Ludwig JA, Booth-Genthe C, Gottesman MM (2006) Targeting multidrug resistance in cancer. Nat Rev Drug Discov 5:219–234. doi: 10.1038/nrd1984 CrossRefPubMedGoogle Scholar
  17. 17.
    Saneja A, Khare V, Alam N, Dubey RD, Gupta PN (2014) Advances in P-glycoprotein-based approaches for delivering anticancer drugs: pharmacokinetic perspective and clinical relevance. Expert Opin Drug Deliv 11:121–138. doi: 10.1517/17425247.2014.865014 CrossRefPubMedGoogle Scholar
  18. 18.
    Saneja A, Dubey RD, Alam N, Khare V, Gupta PN (2014) Co-formulation of P-glycoprotein Substrate and inhibitor in nanocarriers: an emerging strategy for cancer chemotherapy. Curr Cancer Drug Targets 14:419–433. doi: 10.2174/1568009614666140407112034 CrossRefPubMedGoogle Scholar
  19. 19.
    Callaghan R, Luk F, Bebawy M (2014) Inhibition of the multidrug resistance P-glycoprotein: time for a change of strategy? Drug Metab Dispos 42:623–631. doi: 10.1124/dmd.113.056176 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Zhang S, Morris ME (2003) Effects of the flavonoids biochanin A, morin, phloretin, and silymarin on P-glycoprotein-mediated transport. J Pharmacol Exp Ther 304:1258–1267. doi: 10.1124/jpet.102.044412 CrossRefPubMedGoogle Scholar
  21. 21.
    Bansal T, Jaggi M, Khar RK, Talegaonkar S (2009) Emerging significance of flavonoids as P-glycoprotein inhibitors in cancer chemotherapy. J Pharm Pharm Sci 12:46–78. doi: 10.18433/J3RC77 CrossRefPubMedGoogle Scholar
  22. 22.
    Srivalli KMR, Lakshmi PK (2012) Overview of P-glycoprotein inhibitors: a rational outlook. Braz J Pharm Sci 48:353–367. doi: 10.1590/s1984-82502012000300002 CrossRefGoogle Scholar
  23. 23.
    Sak K (2014) Cytotoxicity of dietary flavonoids on different human cancer types. Pharmacogn Rev 8:122–146. doi: 10.4103/0973-7847.134247 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Ferreira A, Pousinho S, Fortuna A, Falcão A, Alves G (2015) Flavonoid compounds as reversal agents of the P-glycoprotein-mediated multidrug resistance: biology, chemistry and pharmacology. Phytochem Rev 14:233–272. doi: 10.1007/s11101-014-9358-0 CrossRefGoogle Scholar
  25. 25.
    Parveen Z, Brunhofer G, Jabeen I, Erker T, Chiba P, Ecker GF (2014) Synthesis, biological evaluation and 3D-QSAR studies of new chalcone derivatives as inhibitors of human P-glycoprotein. Bioorg Med Chem 22:2311–2319. doi: 10.1016/j.bmc.2014.02.005 CrossRefPubMedGoogle Scholar
  26. 26.
    Palmeira A, Sousa E, Vasconcelos MH, Pinto M, Fernandes MX (2012) Structure and ligand-based design of P-glycoprotein inhibitors: a historical perspective. Curr Pharm Des 18:4197–4214. doi: 10.2174/138161212802430530 CrossRefPubMedGoogle Scholar
  27. 27.
    Zdrazil B, Pinto M, Vasanthanathan P, Williams AJ, Balderud LZ, Engkvist O, Chichester C, Hersey A, Overington JP, Ecker GF (2012) Annotating human P-glycoprotein bioassay data. mol inform 31:599–609. doi: 10.1002/minf.201200059 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–1090. doi: 10.1093/nar/gkt1031 CrossRefPubMedGoogle Scholar
  29. 29.
    MOE. 2008.10 edn. Chemical Computing Group Inc., 1010 Sherbrooke St. W, Suite 910, Montreal, Quebec, Canada H3A 2R7. http://www.chemcomp.com/
  30. 30.
    Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–672. doi: 10.1093/nar/gkj067 CrossRefPubMedGoogle Scholar
  31. 31.
    Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:D901–906. doi: 10.1093/nar/gkm958 CrossRefPubMedGoogle Scholar
  32. 32.
    Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (2011) DrugBank 3.0: a comprehensive resource for ’omics’ research on drugs. Nucleic Acids Res 39:D1035–1041. doi: 10.1093/nar/gkq1126 CrossRefPubMedGoogle Scholar
  33. 33.
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–1097. doi: 10.1093/nar/gkt1068 CrossRefPubMedGoogle Scholar
  34. 34.
    ChemBioDrawUltra. 12.0 edn. PerkinElmer, CambridgeSoft. http://www.cambridgesoft.com/
  35. 35.
    Zhang J, Huan J (2010) Comparison of chemical descriptors for protein-chemical interaction prediction. Int J Comput Biosci. doi: 10.2316/Journal.210.2010.1.210-1010
  36. 36.
    Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474. doi: 10.1002/jcc.21707 CrossRefPubMedGoogle Scholar
  37. 37.
    Demel MA, Janecek AGK, Thai K-M, Ecker GF, Gansterer WN (2008) Predictive QSAR Models for polyspecific drug targets: the importance of feature selection. Curr Comput Aided Drug Des 4:91–110. doi: 10.2174/157340908784533256 CrossRefGoogle Scholar
  38. 38.
    RapidMiner. 5.3.008 edn. Rapid-I and contributors, Stockumer Str. 475, 44227 Dortmund, Germany. http://rapidminer.com/
  39. 39.
    WEKA. 3.7.9 edn. The University of Waikato, Hamilton, New Zealand. http://www.cs.waikato.ac.nz/ml/weka/
  40. 40.
    Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33:445–459PubMedGoogle Scholar
  41. 41.
    OECD (2014) Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Publishing, OECD Series on Testing and Assessment. doi: 10.1787/9789264085442-en
  42. 42.
    Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz GY, Perkins R, Roberts D, Schultz T, Stanton DW, van de Sandt JJ, Tong W, Veith G, Yang C (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33:155–173PubMedGoogle Scholar
  43. 43.
    Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab 145:22–29. doi: 10.1016/j.chemolab.2015.04.013 CrossRefGoogle Scholar
  44. 44.
    Roy K, Kar S (2015) Importance of applicability domain of QSAR models. In: Kunal R (ed) Quantitative Structure-activity relationships in drug design, predictive toxicology, and risk assessment. IGI Global, Hershey, pp 180–211. doi: 10.4018/978-1-4666-8136-1.ch005 Google Scholar
  45. 45.
    Dobchev DA, Pillai GG, Karelson M (2014) In silico machine learning methods in drug development. Curr Top Med Chem 14:1913–1922. doi: 10.2174/1568026614666140929124203 CrossRefPubMedGoogle Scholar
  46. 46.
    Clementine. 12.0 edn. SPSS Inc., 233 South Wacker Drive, 11th Floor, Chicago, IL 60606-6307, USA. http://www.spss.com/
  47. 47.
    Witten IH, Frank E, Hall MA (2011) Data mining : practical machine learning tools and techniques. Morgan Kaufmann series in data management systems, 3rd edn. Morgan Kaufmann, BurlingtonGoogle Scholar
  48. 48.
    Bengio Y, Grandvalet Y (2004) No unbiased estimator of the variance of K-fold cross-validation. J Mach Learn Res 5:1089-1105. www.jmlr.org/papers/v5/grandvalet04a.html
  49. 49.
    Pratim Roy P, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. doi: 10.3390/molecules14051660 CrossRefPubMedGoogle Scholar
  50. 50.
    Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20:269–276. doi: 10.1016/S1093-3263(01)00123-1 CrossRefPubMedGoogle Scholar
  51. 51.
    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77. doi: 10.1002/qsar.200390007 CrossRefGoogle Scholar
  52. 52.
    Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701. doi: 10.1002/qsar.200610151 CrossRefGoogle Scholar
  53. 53.
    Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488. doi: 10.1002/minf.201000061 CrossRefPubMedGoogle Scholar
  54. 54.
    Shi LM, Fang H, Tong W, Wu J, Perkins R, Blair RM, Branham WS, Dial SL, Moland CL, Sheehan DM (2001) QSAR models using a large diverse set of estrogens. J Chem Inf Comput Sci 41:186–195. doi: 10.1021/ci000066d CrossRefPubMedGoogle Scholar
  55. 55.
    Schuurmann G, Ebert RU, Chen J, Wang B, Kuhne R (2008) External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J Chem Inf Model 48:2140–2145. doi: 10.1021/ci800253u CrossRefPubMedGoogle Scholar
  56. 56.
    Consonni V, Ballabio D, Todeschini R (2009) Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model 49:1669–1678. doi: 10.1021/ci900115y CrossRefPubMedGoogle Scholar
  57. 57.
    Consonni V, Ballabio D, Todeschini R (2010) Evaluation of model predictive ability by external validation techniques. J Chemom 24:194–201. doi: 10.1002/cem.1290 CrossRefGoogle Scholar
  58. 58.
    Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313. doi: 10.1002/qsar.200710043 CrossRefGoogle Scholar
  59. 59.
    Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemometr Intell Lab 107:194–205. doi: 10.1016/j.chemolab.2011.03.011 CrossRefGoogle Scholar
  60. 60.
    Ojha PK, Roy K (2011) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr Intell Lab 109:146–161. doi: 10.1016/j.chemolab.2011.08.007 CrossRefGoogle Scholar
  61. 61.
    Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268. doi: 10.2307/2532051 CrossRefPubMedGoogle Scholar
  62. 62.
    Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51:2320–2335. doi: 10.1021/ci200211n CrossRefPubMedGoogle Scholar
  63. 63.
    Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52:2044–2058. doi: 10.1021/ci300084j CrossRefPubMedGoogle Scholar
  64. 64.
    Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2011) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408. doi: 10.1021/ci200520g CrossRefGoogle Scholar
  65. 65.
    Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr Intell Lab 152:18–33. doi: 10.1016/j.chemolab.2016.01.008 CrossRefGoogle Scholar
  66. 66.
    Ravna AW, Sylte I (2012) Homology modeling of transporter proteins (carriers and ion channels). Methods Mol Biol 857:281–299. doi: 10.1007/978-1-61779-588-6_12 CrossRefPubMedGoogle Scholar
  67. 67.
    Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinform 9:40. doi: 10.1186/1471-2105-9-40 CrossRefGoogle Scholar
  68. 68.
    Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738. doi: 10.1038/nprot.2010.5 CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol 5:17. doi: 10.1186/1741-7007-5-17 CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Zhang Y (2009) I-TASSER: fully automated protein structure prediction in CASP8. Proteins 77(Suppl 9):100–113. doi: 10.1002/prot.22588 CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Multidrug resistance protein 1 [Homo sapiens] - NCBI Reference Sequence: NP_000918.2. NCBI. http://www.ncbi.nlm.nih.gov/protein/42741659?report=fasta. Accessed 22 Sept 2015
  72. 72.
    Xu J, Zhang Y (2010) How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26:889–895. doi: 10.1093/bioinformatics/btq066 CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Laskowski RA, Macarthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291. doi: 10.1107/S0021889892009944 CrossRefGoogle Scholar
  74. 74.
    LeadIT. 2.0.2 edn. BioSolveIT GmbH, An der Ziegelei 79, 53757 St. Augustin, Germany. http://www.biosolveit.de/
  75. 75.
    Baguley BC (2010) Multiple drug resistance mechanisms in cancer. Mol Biotechnol 46:308–316. doi: 10.1007/s12033-010-9321-2 CrossRefPubMedGoogle Scholar
  76. 76.
    Litman T, Zeuthen T, Skovsgaard T, Stein WD (1997) Structure-activity relationships of P-glycoprotein interacting drugs: kinetic characterization of their effects on ATPase activity. Biochim Biophys Acta 1361:159–168. doi: 10.1016/S0925-4439(97)00026-4 CrossRefPubMedGoogle Scholar
  77. 77.
    Österberg T, Norinder U (2000) Theoretical calculation and prediction of P-glycoprotein-interacting drugs using MolSurf parametrization and PLS statistics. Eur J Pharm Sci 10:295–303. doi: 10.1016/S0928-0987(00)00077-4 CrossRefPubMedGoogle Scholar
  78. 78.
    Dearden JC, Al-Noobi A, Scott AC, Thomson SA (2003) QSAR studies on P-glycoprotein-regulated multidrug resistance and on its reversal by phenothiazines. SAR QSAR Environ Res 14:447–454. doi: 10.1080/10629360310001624024 CrossRefPubMedGoogle Scholar
  79. 79.
    Wang RB, Kuo CL, Lien LL, Lien EJ (2003) Structure-activity relationship: analyses of p-glycoprotein substrates and inhibitors. J Clin Pharm Ther 28:203–228. doi: 10.1046/j.1365-2710.2003.00487.x CrossRefPubMedGoogle Scholar
  80. 80.
    Kupsáková I, Rybár A, Dočolomanský P, Drobná Z, Stein U, Walther W, BarančıÃk M, Breier A (2004) Reversal of P-glycoprotein mediated vincristineresistance of L1210/VCR cells by analogues of pentoxifylline: A QSARstudy. Eur J Pharm Sci 21:283–293. doi: 10.1016/j.ejps.2003.10.019 CrossRefPubMedGoogle Scholar
  81. 81.
    Wang Y-H, Li Y, Yang S-L, Yang L (2005) An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network. J Comput Aided Mol Des 19:137–147. doi: 10.1007/s10822-005-3321-5 CrossRefPubMedGoogle Scholar
  82. 82.
    Wu J, Li X, Cheng W, Xie Q, Liu Y, Zhao C (2009) Quantitative Structure activity relationship (QSAR) approach to multiple drug resistance (MDR) modulators based on combined hybrid system. QSAR Comb Sci 28:969–978. doi: 10.1002/qsar.200860134 CrossRefGoogle Scholar
  83. 83.
    Sousa IJ, Ferreira M-JU, Molnár J, Fernandes MX (2013) QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity. Eur J Pharm Sci 48:542–553. doi: 10.1016/j.ejps.2012.11.012 CrossRefPubMedGoogle Scholar
  84. 84.
    Jabeen I, Wetwitayaklung P, Chiba P, Pastor M, Ecker GF (2013) 2D- and 3D-QSAR studies of a series of benzopyranes and benzopyrano[3,4b][1,4]-oxazines as inhibitors of the multidrug transporter P-glycoprotein. J Comput Aided Mol Des 27:161–171. doi: 10.1007/s10822-013-9635-9 CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Vazquez RN, Camargo AB, Marchevsky EJ, Luco JM (2014) Molecular factors influencing the affinity of flavonoid compounds on P-glycoprotein efflux transporter. Curr Comput Aided Drug Des 10:250–258. doi: 10.2174/157340991003150302231140 CrossRefPubMedGoogle Scholar
  86. 86.
    Shen J, Cui Y, Gu J, Li Y, Li L (2014) A genetic algorithm- back propagation artificial neural network model to quantify the affinity of flavonoids toward P-glycoprotein. Comb Chem High Throughput Screen 17:162–172. doi: 10.2174/1386207311301010002 CrossRefPubMedGoogle Scholar
  87. 87.
    Li J, Jaimes KF, Aller SG (2014) Refined structures of mouse P-glycoprotein. Protein Sci 23:34–46. doi: 10.1002/pro.2387 CrossRefPubMedGoogle Scholar
  88. 88.
    Jin MS, Oldham ML, Zhang Q, Chen J (2012) Crystal structure of the multidrug transporter P-glycoprotein from Caenorhabditis elegans. Nature 490:566–569. doi: 10.1038/nature11448 CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Ward A, Reyes CL, Yu J, Roth CB, Chang G (2007) Flexibility in the ABC transporter MsbA: alternating access with a twist. Proc Natl Acad Sci USA 104:19005–19010. doi: 10.1073/pnas.0709388104 CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Dawson RJ, Locher KP (2006) Structure of a bacterial multidrug ABC transporter. Nature 443:180–185. doi: 10.1038/nature05155 CrossRefPubMedGoogle Scholar
  91. 91.
    Dawson RJ, Locher KP (2007) Structure of the multidrug ABC transporter Sav 1866 from Staphylococcus aureus in complex with AMP-PNP. FEBS Lett 581:935–938. doi: 10.1016/j.febslet.2007.01.073
  92. 92.
    Locher KP, Lee AT, Rees DC (2002) The E. coli BtuCD structure: a framework for ABC transporter architecture and mechanism. Science 296:1091–1098. doi: 10.1126/science.1071142 CrossRefPubMedGoogle Scholar
  93. 93.
    Hvorup RN, Goetz BA, Niederer M, Hollenstein K, Perozo E, Locher KP (2007) Asymmetry in the structure of the ABC transporter-binding protein complex BtuCD-BtuF. Science 317:1387–1390. doi: 10.1126/science.1145950 CrossRefPubMedGoogle Scholar
  94. 94.
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402. doi: 10.1093/nar/25.17.3389 CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Ward AB, Szewczyk P, Grimard V, Lee CW, Martinez L, Doshi R, Caya A, Villaluz M, Pardon E, Cregger C, Swartz DJ, Falson PG, Urbatsch IL, Govaerts C, Steyaert J, Chang G (2013) Structures of P-glycoprotein reveal its conformational flexibility and an epitope on the nucleotide-binding domain. Proc Natl Acad Sci USA 110:13386–13391. doi: 10.1073/pnas.1309275110 CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Rautio J, Humphreys JE, Webster LO, Balakrishnan A, Keogh JP, Kunta JR, Serabjit-Singh CJ, Polli JW (2006) In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates. Drug Metab Dispos 34:786–792. doi: 10.1124/dmd.105.008615 CrossRefPubMedGoogle Scholar
  97. 97.
    Drug Development and Drug Interactions: Table of Substrates, Inhibitors and Inducers. U.S. Food and Drug Administration. http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm093664.htm. Accessed 22 Sept 2015

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Medicinal Chemistry, Faculty of PharmacyUniversity of Medicine and Pharmacy at Ho Chi Minh CityHo Chi Minh CityViet Nam

Personalised recommendations