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:: Volume 19, Issue 1 (3-2017) ::
J Gorgan Univ Med Sci 2017, 19(1): 96-102 Back to browse issues page
Computational approach to the prediction of blood-brain partitioning of basic drug candidates using mixed micellar liquid chromatography
S Arshadi *
Assistant Professor, Department of Chemistry, Payame Noor University, Tehran, Iran , chemistry_arshadi@pnu.ac.ir
Abstract:   (8533 Views)

Background and Objective: The blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system. In this study, the capability of biopartitioning micellar chromatography (BMC) using the mixed micellar system of Brij-35/sodium dodecyl sulfate (Brij-35/SDS, 85:15 mol/mol) has been studied to predict pharmacokinetic parameter (BBB penetration ability) of 14 basic drugs.

Methods: In this descriptive-analytical study, the potential of BMC using mixed micellar system (Brij-35/SDS, 85:15 mol/mol) in 0.04 M at physiological pH 7.4 was evaluated to predict pharmacokinetic parameter (BBB penetration ability) of 14 basic drugs. The regression model for the prediction of blood-brain distribution coefficient is derived from the multiple linear regression analysis using the training set in mixed micellar mobile phase. Also, the predictive ability of model was evaluated for a prediction set of 5 compounds (Chlorpromazine, Mianserin, Propranolol, Cimetidine, and Thioridazine). The fair R2 indicates good stability and predictive ability of the developed model for the drugs not included in modeling.

Results: The relationship between the BMC retention data of 14 basic drugs and their log BB parameter showed a good statistically model (R2=0.822, F=25.42, SE=0.225, R2CV=0.781).

Conclusion: This study points out the usefulness of mixed micellar solution of Brij-35/SDS, 85:15 (mol/mol) in BMC as a high-throughput primary screening tool that can provide key information about the blood-brain distribution of basic drugs in a simple and economical way.

Keywords: Blood–brain barrier, Sodium dodecyl sulfate, Polyoxyethylene (23) lauryl ether (Brij-35), Biopartitioning micellar chromatography
Full-Text [PDF 273 kb] [English Abstract]   (18450 Downloads) |   |   Abstract (HTML)  (1237 Views)  
Type of Study: Original Articles | Subject: Physiology - Pharmacology
References
1. Molero-Monfort M, Escuder-Gilabert L, Villanueva-Camañas RM, Sagrado S, Medina-Hernández MJ. Biopartitioning micellar chromatography: an in vitro technique for predicting human drug absorption. J Chromatogr B Biomed Sci Appl. 2001 Apr; 753(2): 225-36.
2. Odović J, Marković B, Vladimirov S, Karljiković-Rajić K. In vitro modeling of angiotensin-converting enzyme inhibitor's absorption with chromatographic retention data and selected molecular descriptors. J Chromatogr B Analyt Technol Biomed Life Sci. 2014 Mar; 953-54: 102-7. doi:10.1016/j.jchromb.2014.02.004
3. Wang S, Yang G, Zhang H, Liu H, Li Z. QRAR models for cardiovascular system drugs using biopartitioning micellar chromatography. Journal of Chromatography B. 2007 Feb; 846(1-2): 329-33. doi:10.1016/j.jchromb.2006.08.027
4. Escuder-Gilabert L, Molero-Monfort M, Villanueva-Camañas RM, Sagrado S, Medina-Hernández MJ. Potential of biopartitioning micellar chromatography as an in vitro technique for predicting drug penetration across the blood-brain barrier. J Chromatogr B Analyt Technol Biomed Life Sci. 2004 Aug; 807(2): 193-201.
5. Molero-Monfort M, Martín-Biosca Y, Sagrado S, Villanueva-Camañas RM, Medina-Hernández MJ. Micellar liquid chromatography for prediction of drug transport. J Chromatogr A. 2000 Feb; 870(1-2): 1-11.
6. Flaten GE, Palac Z, Engesland A, Filipović-Grčić J, Vanić Ž, Škalko-Basnet N. In vitro skin models as a tool in optimization of drug formulation. Eur J Pharm Sci. 2015 Jul; 75: 10-24. doi:10.1016/j.ejps.2015.02.018
7. Reese TS, Karnovsky MJ. Fine structural localization of a blood-brain barrier to exogenous peroxidase. J Cell Biol. 1967 Jul; 34(1): 207-17.
8. Van Damme S, Langenaeker W, Bultinck P. Prediction of blood-brain partitioning: a model based on ab initio calculated quantum chemical descriptors. J Mol Graph Model. 2008 Jun; 26(8): 1223-36. doi:10.1016/j.jmgm.2007.11.004
9. Norinder U, Haeberlein M. Computational approaches to the prediction of the blood-brain distribution. Adv Drug Deliv Rev. 2002 Mar; 54(3): 291-313.
10. Hadjmohammadi M, Salary M. Biopartitioning micellar chromatography with sodium dodecyl sulfate as a pseudo α(1)-acid glycoprotein to the prediction of protein-drug binding. J Chromatogr B Analyt Technol Biomed Life Sci. 2013 Jan; 912: 50-5. doi:10.1016/j.jchromb.2012.11.020
11. Yin CR, Ma LY, Huang JG, Xu L, Shi ZG. Fast profiling ecotoxicity and skin permeability of benzophenone ultraviolet filters using biopartitioning micellar chromatography based on penetrable silica spheres. Analytica Chimica Acta. 2013; 804: 321-27. doi:10.1016/j.aca.2013.10.040
12. Salary M, Hadjmohammadi M. Human serum albumin-mimetic chromatography based hexadecyltrimethylammonium bromide as a novel direct probe for protein binding of acidic drugs. J Pharm Biomed Anal. 2015 Oct; 114: 1-7. doi:10.1016/j.jpba.2015.04.040
13. Waters LJ, Shahzad Y, Stephenson J. Modelling skin permeability with micellar liquid chromatography. Eur J Pharm Sci. 2013 Nov; 50(3-4): 335-40. doi:10.1016/j.ejps.2013.08.002
14. Wu LP, Chen Y, Wang SR, Chen C, Ye LM. Quantitative retention–activity relationship models for quinolones using biopartitioning micellar chromatography. Biomedical Chromatography. 2008; 22(1): 106-14. doi:10.1002/bmc.907
15. Dobričić V, Nikolic K, Vladimirov S, Čudina O. Biopartitioning micellar chromatography as a predictive tool for skin and corneal permeability of newly synthesized 17β-carboxamide steroids. Eur J Pharm Sci. 2014 Jun; 56: 105-12. doi:10.1016/j.ejps.2014.02.007
16. Stępnik KE, Malinowska I, Rój E. In vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography. Talanta. 2014 Dec; 130: 265-73. doi:10.1016/j.talanta.2014.06.039
17. Stępnik KE, Malinowska I. The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols. J Chromatogr A. 2013 Apr; 1286: 127-36. doi:10.1016/j.chroma.2013.02.071
18. Jäckle J. The causal theory of the resting potential of cells. J Theor Biol. 2007 Dec; 249(3): 445-63.
19. Wu LP, Cui Y, Xiong MJ, Wang SR, Chen C, Ye LM. Mixed micellar liquid chromatography methods: modelling quantitative retention-activity relationships of angiotensin converting enzyme inhibitors. Biomed Chromatogr. 2008 Nov; 22(11): 1243-51. doi:10.1002/bmc.1053
20. Chen Y, Wu LP, Chen C, Ye LM. Development of predictive quantitative retention–activity relationship models of alkaloids by mixed micellar liquid chromatography. Biomedical Chromatography. 2009; 24(2): 195-201. doi:10.1002/bmc.1272
21. dos Santos WL, Rahman J, Klein N, Male DK. Distribution and analysis of surface charge on brain endothelium in vitro and in situ. Acta Neuropathol. 1995; 90(3): 305-11.
22. Wu LP, Chen C, Sun CJ, Ye LM. QRAR Models for Diuretics using mixed micellar liquid chromatography. J Bioequiv Availab. 2011; 3(7): 169-73. doi:10.4172/jbb.1000079
23. Wold S. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics. 1978 Nov; 20(4): 397-405. doi:10.2307/1267639
24. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988 Aug; 110(18): 5959-67. doi:10.1021/ja00226a005
25. Osten DW. Selection of optimal regression models via cross-validation. Journal of Chemometrics. 1988 Jan; 2(1): 39-48. doi:10.1002/cem.1180020106
26. Feher M, Sourial E, Schmidt JM. A simple model for the prediction of blood-brain partitioning. Int J Pharm. 2000 May; 201(2): 239-47.
27. Quiٌones-Torrelo C, Sagrado S, Villanueva-Camaٌas RM, Medina- Hernández MJ. Development of predictive retention−activity relationship models of tricyclic antidepressants by micellar liquid chromatography. J Med Chem. 1999; 42(16): 3154-62. doi:10.1021/jm9910369
28. Wan H, Ahman M, Holmén AG. Relationship between brain tissue partitioning and microemulsion retention factors of CNS drugs. J Med Chem. 2009 Mar; 52(6): 1693-700. doi:10.1021/jm801441s
29. Martı́nez-Pla JJ, Sagrado S, Villanueva-Camañas RM, Medina-Hernández MJ. Retention–property relationships of anticonvulsant drugs by biopartitioning micellar chromatography. Journal of Chromatography B: Biomedical Sciences and Applications. 2001; 757 (1): 89–99. doi:10.1016/ S0378-4347(01)00124-4
30. Liu J, Sun J, Sui X, Wang Y, Hou Y, He Z. Predicting blood–brain barrier penetration of drugs by microemulsion liquid chromatography with corrected retention factor. J Chromatogr A. 2008; 1198-99: 164-72. doi:10.1016/j.chroma.2008.05.065
31. Platts JA, Abraham MH, Zhao YH, Hersey A, Ijaz L, Butina D. Correlation and prediction of a large blood-brain distribution data set--an LFER study. Eur J Med Chem. 2001 Sep; 36(9): 719-30.
32. Vilar S, Chakrabarti M, Costanzi S. Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors. J Mol Graph Model. 2010 Jun; 28(8): 899-903. doi:10.1016/j.jmgm.2010.03.010
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Arshadi S. Computational approach to the prediction of blood-brain partitioning of basic drug candidates using mixed micellar liquid chromatography . J Gorgan Univ Med Sci 2017; 19 (1) :96-102
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Volume 19, Issue 1 (3-2017) Back to browse issues page
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