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Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability

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Abstract

Nowadays most of the CNS acting therapeutic molecules are failing in clinical trials due to efflux transporters at the blood brain barrier (BBB) which imparts resistance and poor ADMET properties of these molecules. CNS acting drug molecules interact with the BBB prior to their target site, so there is a need to develop predictive models for BBB permeability which can be used in the initial phases of drug discovery process. Most of the drug molecules are transported to the brain via passive diffusion which is explored extensively; on the other hand, the role of active efflux transporters in BBB permeability is unclear. Our aim is to develop predictive models for BBB permeability that include active efflux transporters. An in silico model has been developed to assess the role of BCRP on BBB permeation. Eight descriptors were selected, which also include BCRP substrate probabilities used for model development and show a relationship between BCRP and logBB. From our analysis, it was found that 11 molecules satisfied all criteria required for BBB permeation but have low logBB values. These 11 molecules are predicted as BCRP substrates from the model developed, suggesting that the molecules are effluxed by the BCRP transporter. This predictive ability was further validated by docking of these 11 molecules into BCRP protein. This study provides a new mechanistic insight into correlation of low logBB values and efflux mechanism of BCRP in BBB.

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References

  1. Davson H, Segal MB (1996) Physiology of the CSF and blood-brain barriers. CRC Press, Boca Raton

    Google Scholar 

  2. Kalvass JC, Maurer TS (2002) Influence of nonspecific brain and plasma binding on CNS exposure: implications for rational drug discovery. Biopharm Drug Dispos 23:327–338. doi:10.1002/bdd.325

    Article  CAS  PubMed  Google Scholar 

  3. Reichel A (2006) The role of blood-brain barrier studies in the pharmaceutical industry. Curr Drug Metab 7:183–203. doi:10.2174/138920006775541525

    Article  CAS  PubMed  Google Scholar 

  4. Mehdipour AR, Hamidi M (2009) Brain drug targeting: a computational approach for overcoming blood-brain barrier. Drug Discov Today 14:1030–1036. doi:10.1016/j.drudis.2009.07.009

    Article  CAS  PubMed  Google Scholar 

  5. Bickel U (2005) How to measure drug transport across the blood-brain barrier. NeuroRx 2:15–26. doi:10.1602/neurorx.2.1.15

    Article  PubMed Central  PubMed  Google Scholar 

  6. Begley DJ (2004) Delivery of therapeutic agents to the central nervous system: the problems and the possibilities. Pharmacol Ther 104:29–45. doi:10.1016/j.pharmthera.2004.08.001

    Article  CAS  PubMed  Google Scholar 

  7. Scala S, Akhmed N, Rao US, Paull K, Lan LB, Dickstein B, Lee JS, Elgemeie GH, Stein WD, Bates SE (1997) P-glycoprotein substrates and antagonists cluster into two distinct groups. Mol Pharmacol 51:1024–1033

    CAS  PubMed  Google Scholar 

  8. Gleeson MP (2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 51:817–834. doi:10.1021/jm701122q

    Article  CAS  PubMed  Google Scholar 

  9. Muehlbacher M, Spitzer GM, Liedl KR, Kornhuber J (2011) Qualitative prediction of blood-brain barrier permeability on a large and refined dataset. J Comput Aided Mol Des 25:1095–1106. doi:10.1007/s10822-011-9478-1

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Cuadrado MU, Ruiz IL, Gómez-Nieto MA (2007) QSAR models based on isomorphic and nonisomorphic data fusion for predicting the blood brain barrier permeability. J Comput Chem 28:1252–1260. doi:10.1002/jcc.20671

    Article  CAS  PubMed  Google Scholar 

  11. Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A (2008) QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm Res 25:1902–1914. doi:10.1007/s11095-008-9609-0

    Article  CAS  PubMed  Google Scholar 

  12. Obrezanova O, Gola JMR, Champness EJ, Segall MD (2008) Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J Comput Aided Mol Des 22:431–440. doi:10.1007/s10822-008-9193-8

    Article  CAS  PubMed  Google Scholar 

  13. Moda TL, Carrara AE, Andricopulo AD (2013) A fragment-based approach for the in silico prediction of blood-brain barrier permeation. J Braz Chem Soc 23:2191–2196. doi:10.1590/S0103-50532013005000001

    Article  Google Scholar 

  14. Fan Y, Unwalla R, Denny RA, Di L, Kerns EH, Diller DJ, Humblet C (2010) Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches. J Chem Inf Model 50:1123–1133. doi:10.1021/ci900384c

    Article  CAS  PubMed  Google Scholar 

  15. Shen J, Du Y, Zhao Y, Liu G, Tang Y (2008) In silico prediction of blood-brain partitioning using a chemometric method called genetic algorithm based variable selection. QSAR Comb Sci 27:704–717. doi:10.1002/qsar.200710129

    Article  CAS  Google Scholar 

  16. Lanevskij K, Japertas P, Didziapetris R, Petrauskas A (2009) Ionization specific prediction of blood-brain permeability. J Pharm Sci 98:122–134. doi:10.1002/jps.21405

    Article  CAS  PubMed  Google Scholar 

  17. Konovalov DA, Coomans D, Deconinck E, Vander Heyden Y (2007) Benchmarking of QSAR models for blood-brain barrier permeation. J Chem Inf Model 47:1648–1656. doi:10.1021/ci700100f

  18. Guerra A, Paez JA, Campillo NE (2008) Artificial neural networks in ADMET modeling: prediction of blood-brain barrier permeation. QSAR Comb Sci 27:586–594. doi:10.1002/qsar.200710019

    Article  CAS  Google Scholar 

  19. Yan A, Liang H, Chong Y, Nie X, Yu C (2012) In-silico prediction of blood-brain barrier permeability. SAR QSAR Environ Res 24:61–74. doi:10.1080/1062936X.2012.729224

    Article  PubMed  Google Scholar 

  20. Bergstrom CAS, Charman SA, Nicolazzo JA (2012) Computational prediction of CNS drug exposure based on a novel in vivo dataset. Pharm Res 29:3131–3142. doi:10.1007/s11095-012-0806-5

    Article  PubMed  Google Scholar 

  21. Garg P, Verma J (2006) In silico prediction of blood brain barrier permeability: an artificial neural network model. J Chem Inf Model 46:289–297. doi:10.1021/ci050303i

    Article  CAS  PubMed  Google Scholar 

  22. Chen Y, Zhu QJ, Pan J, Yang Y, Wu XP (2009) A prediction model for blood-brain barrier permeation and analysis on its parameter biologically. Comput Methods Progr Biomed 95:280–287. doi:10.1016/j.cmpb.2009.03.006

    Article  Google Scholar 

  23. Kortagere S, Chekmarev D, Welsh WJ, Ekins S (2008) New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm Res 25:1836–1845. doi:10.1007/s11095-008-9584-5

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Clark DE (2003) In silico prediction of blood-brain barrier permeation. Drug Discov Today 8:927–933. doi:10.1016/S1359-6446(03)02827-7

    Article  CAS  PubMed  Google Scholar 

  25. Zhong L, Ma CY, Zhang H, Yang LJ, Wan HL, Xie QQ, Li LL, Yang SY (2011) A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method. Comput Biol Med 41:1006–1013. doi:10.1016/j.compbiomed.2011.08.009

    Article  CAS  PubMed  Google Scholar 

  26. Hazai E, Hazai I, Ragueneau-Majlessi I, Chung SP, Bikadi Z, Mao Q (2013) Predicting substrates of the human breast cancer resistance protein using a support vector machine method. BMC Bioinform 14:130. doi:10.1186/1471-2105-14-130

    Article  Google Scholar 

  27. SYBYL molecular modeling system, Tripos Associate 2006, version 7.1

  28. Powell MJD (1977) Restart procedures for the conjugate gradient method. Math Prog 12:241–254. doi:10.1007/BF01593790

    Article  Google Scholar 

  29. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron 36:3219–3228. doi:10.1016/0040-4020(80)80168-2

    Article  CAS  Google Scholar 

  30. Witten IH, Frank E, Hall MA, Holmes G (2011) Data mining, practical machine learning tools and techniques. The MIT Press, Cambridge, MA

    Google Scholar 

  31. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learning 20:273–297. doi:10.1007/BF00994018

    Google Scholar 

  32. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167. doi:10.1023/A:1009715923555

    Article  Google Scholar 

  33. Perez JJ (2005) Managing molecular diversity. Chem Soc Rev 34:143–152. doi:10.1039/B209064N

    Article  CAS  PubMed  Google Scholar 

  34. Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996. doi:10.1021/ci9800211

  35. Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. Altern Lab Anim 33:155–173

  36. Gupta P, Sharma A, Garg P, Roy N (2013) QSAR study of curcumine derivatives as HIV-1 integrase inhibitors. Curr Comput Aided Drug Des 9:141–150. doi:10.2174/157340913804998793

  37. Platts JA, Abraham MH, Zhao YH, Hersey A, Ijaz L, Butina D (2001) Correlation and prediction of a large blood-brain distribution data set-an LFER study. Eur J Med Chem 36:719–730. doi:10.1016/S0223-5234(01)01269-7

  38. Rose K, Hall LH, Kier LB (2002) Modeling blood-brain barrier partitioning using the electrotopological state. J Chem Inf Comput Sci 42:651–666. doi:10.1021/ci010127n

    Article  CAS  PubMed  Google Scholar 

  39. ChemSilico CSBBB training—set compounds. http://www.chemsilico.com/CS_prBBB/BBBcomp.html

  40. ChemSilico; CSBBB external validation set compounds. http://www.chemsilico.com/CS_prBBB/BBBExValcomp.html

  41. Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. MIT Press, Cambridge, MA

    Google Scholar 

  42. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759. doi:10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  43. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  44. Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487. doi:10.1021/jp003919d

    Article  CAS  Google Scholar 

  45. Hazai E, Bikadi Z (2008) Homology modeling of breast cancer resistance protein (ABCG2). J Struct Biol 162:63–74. doi:10.1016/j.jsb.2007.12.001

    Article  CAS  PubMed  Google Scholar 

  46. Robey RW, Honjo Y, Morisaki K, Nadjem TA, Runge S, Risbood M, Poruchynsky MS, Bates SE (2003) Mutations at amino-acid 482 in the ABCG2 gene affect substrate and antagonist specificity. Br J Cancer 89:1971–1978. doi:10.1038/sj.bjc.6601370

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. Cai X, Bikadi Z, Ni Z, Lee EW, Wang H, Rosenberg MF, Mao Q (2010) Role of basic residues within or near the predicted transmembrane helix 2 of the human breast cancer resistance protein in drug transport. J Pharmacol Exp Ther 333:670–681. doi:10.1124/jpet.109.163493

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

This work was supported by the Department of Electronics and Information Technology, GOI, New Delhi, India [Grant File No. DIT/R&D/Bio/15(3)/ 2011]. The authors acknowledge Ms. Kanwaljit Kaur for her valuable inputs.

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  1. Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S.A.S. Nagar, Punjab, 160062, India

    Prabha Garg, Rahul Dhakne & Vilas Belekar

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  1. Prabha Garg
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Correspondence to Prabha Garg.

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Garg, P., Dhakne, R. & Belekar, V. Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability. Mol Divers 19, 163–172 (2015). https://doi.org/10.1007/s11030-014-9562-2

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