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Impact of In Vitro Passive Permeability in a P-gp-transfected LLC-PK1 Model on the Prediction of the Rat and Human Unbound Brain-to-Plasma Concentration Ratio

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Abstract

Purpose

More accurate prediction of the extent of drug brain exposure in early drug discovery and understanding potential species differences could help to guide medicinal chemistry and avoid unnecessary animal studies. Hence, the aim of the current study was to validate the use of a P-gp transfected LLC-PK1 model to predict the unbound brain-to-plasma concentration ratio (Kpuu,brain) in rats and humans.

Methods

MOCK-, Mdr1a- and MDR1-transfected LLC-PK1 monolayers were applied in a transwell setup to quantify the bidirectional transport for 12 specific P-gp substrates, 48 UCB drug discovery compounds, 11 compounds with reported rat in situ brain perfusion data and 6 compounds with reported human Kpuu,brain values. The in vitro transport data were introduced in a minimal PBPK model (SIVA®) to determine the transport parameters. These parameters were combined with the differences between in vitro and in vivo passive permeability as well as P-gp expression levels (as determined by LC-MS/MS), to predict the Kpuu,brain.

Results

A 10-fold difference between in vitro and in vivo passive permeability was observed. Incorporation of the differences between in vitro and in vivo passive permeability and P-gp expression levels resulted in an improved prediction of rat (AAFE 2.17) and human Kpuu,brain (AAFE 2.10).

Conclusions

We have succesfully validated a methodology to use a P-gp overexpressing LLC-PK1 cell line to predict both rat and human Kpuu,brain by correcting for both passive permeability and P-gp expression levels.

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Abbreviations

BBB :

Blood-brain barrier

CNS :

Central nervous system

ER :

Efflux ratio

Kpuu,brain :

Unbound brain-to-plasma concentration ratio

LLC-PK1 :

Lilly-laboratories Cell-Porcine Kidney 1

MDR1/Mdr1a :

Human/rat multidrug resistance

nER :

net Efflux ratio

Papp :

Apparent permeability coefficient

P-gp :

P-glycoprotein

PS :

Passive permeability surface area product

References

  1. Johnson IP. Age-related neurodegenerative disease research needs aging models. Front Aging Neurosci. 2015;7:168.

    PubMed  PubMed Central  Google Scholar 

  2. Gribkoff VK, Kaczmarek LK. The need for new approaches in CNS drug discovery: why drugs have failed, and what can be done to improve outcomes. Neuropharmacology. 2017;120:11–9.

    CAS  PubMed  Google Scholar 

  3. Pardridge WM. Drug transport across the blood-brain barrier. J Cereb Blood Flow Metab. 2012;32(11):1959–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Hammarlund-Udenaes M, Lange ECMd, Thorne RG, American Association of Pharmaceutical Scientists. Drug delivery to the brain : physiological concepts, methodologies, and approaches. New York: AAPS Press : Springer; 2014. xx, 731 pages p.

  5. Nicholls G, Youdim K, Royal Society of Chemistry (Great Britain). Drug transporters. Cambridge: Royal Society Of Chemistry; 2016. 2 volumes p.

  6. Di L, Rong H, Feng B. Demystifying brain penetration in central nervous system drug discovery. Miniperspective J Med Chem. 2013;56(1):2–12.

    CAS  PubMed  Google Scholar 

  7. International Transporter C, Giacomini KM, Huang SM, Tweedie DJ, Benet LZ, Brouwer KL, et al. Membrane transporters in drug development. Nat Rev Drug Discov. 2010;9(3):215–36.

    Google Scholar 

  8. Cheng ZL, Q. Uptake Transport a the BBB - Examples and SAR. In: Li Di EHK, editor. Blood-Brain Barrier in Drug Discovery: Wiley; 2015. p. 586.

  9. Hoshi Y, Uchida Y, Tachikawa M, Inoue T, Ohtsuki S, Terasaki T. Quantitative atlas of blood-brain barrier transporters, receptors, and tight junction proteins in rats and common marmoset. J Pharm Sci. 2013;102(9):3343–55.

    CAS  PubMed  Google Scholar 

  10. Aday S, Cecchelli R, Hallier-Vanuxeem D, Dehouck MP, Ferreira L. Stem cell-based human blood-brain barrier models for drug discovery and delivery. Trends Biotechnol. 2016;34(5):382–93.

    CAS  PubMed  Google Scholar 

  11. Mahringer A, Fricker G. ABC transporters at the blood-brain barrier. Expert Opin Drug Metab Toxicol. 2016;12(5):499–508.

    CAS  PubMed  Google Scholar 

  12. Hammarlund-Udenaes M, Friden M, Syvanen S, Gupta A. On the rate and extent of drug delivery to the brain. Pharm Res. 2008;25(8):1737–50.

    CAS  PubMed  Google Scholar 

  13. Liu X, Chen C, Smith BJ. Progress in brain penetration evaluation in drug discovery and development. J Pharmacol Exp Ther. 2008;325(2):349–56.

    CAS  PubMed  Google Scholar 

  14. Liu H, Li Y, Lu S, Wu Y, Sahi J. Temporal expression of transporters and receptors in a rat primary co-culture blood-brain barrier model. Xenobiotica. 2014;44(10):941–51.

    CAS  PubMed  Google Scholar 

  15. Kaisar MA, Sajja RK, Prasad S, Abhyankar VV, Liles T, Cucullo L. New experimental models of the blood-brain barrier for CNS drug discovery. Expert Opin Drug Discov. 2017;12(1):89–103.

    PubMed  Google Scholar 

  16. Helms HC, Abbott NJ, Burek M, Cecchelli R, Couraud PO, Deli MA, et al. In vitro models of the blood-brain barrier: an overview of commonly used brain endothelial cell culture models and guidelines for their use. J Cereb Blood Flow Metab. 2016;36(5):862–90.

  17. Liu H, Dong K, Zhang W, Summerfield SG, Terstappen GC. Prediction of brain:blood unbound concentration ratios in CNS drug discovery employing in silico and in vitro model systems. Drug Discov Today. 2018;23(7):1357–72.

  18. Hakkarainen JJ, Jalkanen AJ, Kaariainen TM, Keski-Rahkonen P, Venalainen T, Hokkanen J, et al. Comparison of in vitro cell models in predicting in vivo brain entry of drugs. Int J Pharm. 2010;402(1–2):27–36.

  19. Caruso A, Alvarez-Sanchez R, Hillebrecht A, Poirier A, Schuler F, Lave T, et al. PK/PD assessment in CNS drug discovery: prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation. Biochem Pharmacol. 2013;85(11):1684–99.

  20. Poirier A, Cascais AC, Bader U, Portmann R, Brun ME, Walter I, et al. Calibration of in vitro multidrug resistance protein 1 substrate and inhibition assays as a basis to support the prediction of clinically relevant interactions in vivo. Drug Metab Dispos. 2014;42(9):1411–22.

  21. Adachi Y, Suzuki H, Sugiyama Y. Comparative studies on in vitro methods for evaluating in vivo function of MDR1 P-glycoprotein. Pharm Res. 2001;18(12):1660–8.

  22. Summerfield SG, Stevens AJ, Cutler L, del Carmen OM, Hammond B, Tang SP, et al. Improving the in vitro prediction of in vivo central nervous system penetration: integrating permeability, P-glycoprotein efflux, and free fractions in blood and brain. J Pharmacol Exp Ther. 2006;316(3):1282–90.

  23. Feng B, Mills JB, Davidson RE, Mireles RJ, Janiszewski JS, Troutman MD, et al. In vitro P-glycoprotein assays to predict the in vivo interactions of P-glycoprotein with drugs in the central nervous system. Drug Metab Dispos. 2008;36(2):268–75.

  24. Uchida Y, Ohtsuki S, Kamiie J, Terasaki T. Blood-brain barrier (BBB) pharmacoproteomics: reconstruction of in vivo brain distribution of 11 P-glycoprotein substrates based on the BBB transporter protein concentration, in vitro intrinsic transport activity, and unbound fraction in plasma and brain in mice. J Pharmacol Exp Ther. 2011;339(2):579–88.

  25. Uchida Y, Wakayama K, Ohtsuki S, Chiba M, Ohe T, Ishii Y, et al. Blood-brain barrier pharmacoproteomics-based reconstruction of the in vivo brain distribution of P-glycoprotein substrates in cynomolgus monkeys. J Pharmacol Exp Ther. 2014;350(3):578–88.

  26. Trapa PE, Troutman MD, Lau TY, Wager TT, Maurer TS, Patel NC, et al. In vitro-in vivo extrapolation of key transporter activity at the blood-brain barrier. Drug Metab Dispos. 2019;47(4):405–11.

  27. Di L, Kerns EH, Bezar IF, Petusky SL, Huang Y. Comparison of blood-brain barrier permeability assays: in situ brain perfusion, MDR1-MDCKII and PAMPA-BBB. J Pharm Sci. 2009;98(6):1980–91.

    CAS  PubMed  Google Scholar 

  28. Pardridge WM, Triguero D, Yang J, Cancilla PA. Comparison of in vitro and in vivo models of drug transcytosis through the blood-brain barrier. J Pharmacol Exp Ther. 1990;253(2):884–91.

  29. Takasato Y, Rapoport SI, Smith QR. An in situ brain perfusion technique to study cerebrovascular transport in the rat. Am J Phys. 1984;247(3 Pt 2):H484–93.

    CAS  Google Scholar 

  30. Kamiie J, Ohtsuki S, Iwase R, Ohmine K, Katsukura Y, Yanai K, et al. Quantitative atlas of membrane transporter proteins: development and application of a highly sensitive simultaneous LC/MS/MS method combined with novel in-silico peptide selection criteria. Pharm Res. 2008;25(6):1469–83.

    CAS  PubMed  Google Scholar 

  31. Burt HJ HM, Neuhoff S, Machavaram KK, Cain T, Rose R, Feng K, Wedagedera J, Patel N, Warhurst G, Barter ZE, Gardner I, Jamei M, Rostami-Hodjegan A. Challenges in the modelling of bidirectional transport experiments 2014 [Available from: https://www.certara.com/wp-content/uploads/Resources/Posters/Burt_2014_Marbach_transport.pdf].

  32. Korzekwa KR, Nagar S, Tucker J, Weiskircher EA, Bhoopathy S, Hidalgo IJ. Models to predict unbound intracellular drug concentrations in the presence of transporters. Drug Metab Dispos. 2012;40(5):865–76.

    CAS  PubMed  Google Scholar 

  33. Deo AK, Theil FP, Nicolas JM. Confounding parameters in preclinical assessment of blood-brain barrier permeation: an overview with emphasis on species differences and effect of disease states. Mol Pharm. 2013;10(5):1581–95.

    CAS  PubMed  Google Scholar 

  34. Li J, Wang Y, Hidalgo IJ. Kinetic analysis of human and canine P-glycoprotein-mediated drug transport in MDR1-MDCK cell model: approaches to reduce false-negative substrate classification. J Pharm Sci. 2013;102(9):3436–46.

    CAS  PubMed  Google Scholar 

  35. Hellinger E, Veszelka S, Toth AE, Walter F, Kittel A, Bakk ML, et al. Comparison of brain capillary endothelial cell-based and epithelial (MDCK-MDR1, Caco-2, and VB-Caco-2) cell-based surrogate blood-brain barrier penetration models. Eur J Pharm Biopharm. 2012;82(2):340–51.

    CAS  PubMed  Google Scholar 

  36. Kreisl WC, Liow JS, Kimura N, Seneca N, Zoghbi SS, Morse CL, et al. P-glycoprotein function at the blood-brain barrier in humans can be quantified with the substrate radiotracer 11C-N-desmethyl-loperamide. J Nucl Med. 2010;51(4):559–66.

    CAS  PubMed  Google Scholar 

  37. Guo Y, Chu X, Parrott NJ, Brouwer KLR, Hsu V, Nagar S, et al. Advancing predictions of tissue and intracellular drug concentrations using in vitro, imaging and physiologically based pharmacokinetic modeling approaches. Clin Pharmacol Ther. 2018;104(5):865–89.

  38. Dollery CT. Intracellular drug concentrations. Clin Pharmacol Ther. 2013;93(3):263–6.

    CAS  PubMed  Google Scholar 

  39. Liu X, Vilenski O, Kwan J, Apparsundaram S, Weikert R. Unbound brain concentration determines receptor occupancy: a correlation of drug concentration and brain serotonin and dopamine reuptake transporter occupancy for eighteen compounds in rats. Drug Metab Dispos. 2009;37(7):1548–56.

    CAS  PubMed  Google Scholar 

  40. Watson J, Wright S, Lucas A, Clarke KL, Viggers J, Cheetham S, et al. Receptor occupancy and brain free fraction. Drug Metab Dispos. 2009;37(4):753–60.

    CAS  PubMed  Google Scholar 

  41. Kikuchi R, de Morais SM, Kalvass JC. In vitro P-glycoprotein efflux ratio can predict the in vivo brain penetration regardless of biopharmaceutics drug disposition classification system class. Drug Metab Dispos. 2013;41(12):2012–7.

  42. Weekes MP, Antrobus R, Lill JR, Duncan LM, Hor S, Lehner PJ. Comparative analysis of techniques to purify plasma membrane proteins. J Biomol Tech. 2010;21(3):108–15.

    PubMed  PubMed Central  Google Scholar 

  43. Fenneteau F, Turgeon J, Couture L, Michaud V, Li J, Nekka F. Assessing drug distribution in tissues expressing P-glycoprotein through physiologically based pharmacokinetic modeling: model structure and parameters determination. Theor Biol Med Model. 2009;6:2.

    PubMed  PubMed Central  Google Scholar 

  44. Morita SY, Ikeda N, Horikami M, Soda K, Ishihara K, Teraoka R, et al. Effects of phosphatidylethanolamine N-methyltransferase on phospholipid composition, microvillus formation and bile salt resistance in LLC-PK1 cells. FEBS J. 2011;278(24):4768–81.

    CAS  PubMed  Google Scholar 

  45. Oorts M, Lysiane R, Pieter A. Hepatic uptake of domperidone in rat and human suspended hepatocytes. 2015. [Available from: https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS730285&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US&fromSitemap=1].

  46. Friden M, Ljungqvist H, Middleton B, Bredberg U, Hammarlund-Udenaes M. Improved measurement of drug exposure in the brain using drug-specific correction for residual blood. J Cereb Blood Flow Metab. 2010;30(1):150–61.

    CAS  PubMed  Google Scholar 

  47. Prasad B, Achour B, Artursson P, Hop C, Lai Y, Smith PC, et al. Toward a consensus on applying quantitative liquid chromatography-tandem mass spectrometry proteomics in translational pharmacology research: a white paper. Clin Pharmacol Ther. 2019;106(3):525–43.

    PubMed  Google Scholar 

  48. Chen B, Liu L, Ho H, Chen Y, Yang Z, Liang X, et al. Correction to: strategies of drug transporter quantitation by LC-MS: importance of peptide selection and digestion efficiency. AAPS J. 2018;20(4):75.

    PubMed  Google Scholar 

  49. Murakami H, Takanaga H, Matsuo H, Ohtani H, Sawada Y. Comparison of blood-brain barrier permeability in mice and rats using in situ brain perfusion technique. Am J Physiol Heart Circ Physiol. 2000;279(3):H1022–8.

    CAS  PubMed  Google Scholar 

  50. Taylor EM. Efflux transporters and the blood-brain barrier. New York: Nova Biomedical Books; 2005. 247 p. p.

  51. Takeuchi T, Yoshitomi S, Higuchi T, Ikemoto K, Niwa S, Ebihara T, et al. Establishment and characterization of the transformants stably-expressing MDR1 derived from various animal species in LLC-PK1. Pharm Res. 2006;23(7):1460–72.

    CAS  PubMed  Google Scholar 

  52. Syvanen S, Lindhe O, Palner M, Kornum BR, Rahman O, Langstrom B, et al. Species differences in blood-brain barrier transport of three positron emission tomography radioligands with emphasis on P-glycoprotein transport. Drug Metab Dispos. 2009;37(3):635–43.

    PubMed  Google Scholar 

  53. Henthorn TK, Liu Y, Mahapatro M, Ng KY. Active transport of fentanyl by the blood-brain barrier. J Pharmacol Exp Ther. 1999;289(2):1084–9.

    CAS  PubMed  Google Scholar 

  54. Chapy H, Saubamea B, Tournier N, Bourasset F, Behar-Cohen F, Decleves X, et al. Blood-brain and retinal barriers show dissimilar ABC transporter impacts and concealed effect of P-glycoprotein on a novel verapamil influx carrier. Br J Pharmacol. 2016;173(3):497–510.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Friden M. Development of methods for assessing unbound drug exposure in the brain. In vivo, in vitro and in silico. Uppsala: Upssala University; 2010.

  56. de Lange EC. Utility of CSF in translational neuroscience. J Pharmacokinet Pharmacodyn. 2013;40(3):315–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Hsiao P, Unadkat JD. P-glycoprotein-based loperamide-cyclosporine drug interaction at the rat blood-brain barrier: prediction from in vitro studies and extrapolation to humans. Mol Pharm. 2012;9(3):629–33.

  58. Chu X, Zhang Z, Yabut J, Horwitz S, Levorse J, Li XQ, et al. Characterization of multidrug resistance 1a/P-glycoprotein knockout rats generated by zinc finger nucleases. Mol Pharmacol. 2012;81(2):220–7.

    CAS  PubMed  Google Scholar 

  59. Badhan RK, Chenel M, Penny JI. Development of a physiologically-based pharmacokinetic model of the rat central nervous system. Pharmaceutics. 2014;6(1):97–136.

    PubMed  PubMed Central  Google Scholar 

  60. Gratton JA, Abraham MH, Bradbury MW, Chadha HS. Molecular factors influencing drug transfer across the blood-brain barrier. J Pharm Pharmacol. 1997;49(12):1211–6.

    CAS  PubMed  Google Scholar 

  61. Liu X, Tu M, Kelly RS, Chen C, Smith BJ. Development of a computational approach to predict blood-brain barrier permeability. Drug Metab Dispos. 2004;32(1):132–9.

    CAS  PubMed  Google Scholar 

  62. Pardridge WM, Mietus LJ. Transport of steroid hormones through the rat blood-brain barrier. Primary role of albumin-bound hormone. J Clin Invest. 1979;64(1):145–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Summerfield SG, Read K, Begley DJ, Obradovic T, Hidalgo IJ, Coggon S, et al. Central nervous system drug disposition: the relationship between in situ brain permeability and brain free fraction. J Pharmacol Exp Ther. 2007;322(1):205–13.

    CAS  PubMed  Google Scholar 

  64. Passchier J, van Waarde A, Pieterman RM, Elsinga PH, Pruim J, Hendrikse HN, et al. In vivo delineation of 5-HT1A receptors in human brain with [18F]MPPF. J Nucl Med. 2000;41(11):1830–5.

  65. Farde L, Hall H, Ehrin E, Sedvall G. Quantitative analysis of D2 dopamine receptor binding in the living human brain by PET. Science. 1986;231(4735):258–61.

    CAS  PubMed  Google Scholar 

  66. Mawlawi O, Martinez D, Slifstein M, Broft A, Chatterjee R, Hwang DR, et al. Imaging human mesolimbic dopamine transmission with positron emission tomography: I. Accuracy and precision of D(2) receptor parameter measurements in ventral striatum. J Cereb Blood Flow Metab. 2001;21(9):1034–57.

    CAS  PubMed  Google Scholar 

  67. Perlmutter JS, Stambuk MK, Markham J, Black KJ, McGee-Minnich L, Jankovic J, et al. Decreased [18F]spiperone binding in putamen in dystonia. Adv Neurol. 1998;78:161–8.

    CAS  PubMed  Google Scholar 

  68. Wagner CC, Bauer M, Karch R, Feurstein T, Kopp S, Chiba P, et al. A pilot study to assess the efficacy of tariquidar to inhibit P-glycoprotein at the human blood-brain barrier with (R)-11C-verapamil and PET. J Nucl Med. 2009;50(12):1954–61.

    PubMed  Google Scholar 

  69. Bergstrom M, Yates R, Wall A, Kagedal M, Syvanen S, Langstrom B. Blood-brain barrier penetration of zolmitriptan--modelling of positron emission tomography data. J Pharmacokinet Pharmacodyn. 2006;33(1):75–91.

    PubMed  Google Scholar 

  70. Summerfield SG, Lucas AJ, Porter RA, Jeffrey P, Gunn RN, Read KR, et al. Toward an improved prediction of human in vivo brain penetration. Xenobiotica. 2008;38(12):1518–35.

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Acknowledgments

The authors would like to express their gratitude to Julie Maron for maintaining the cell culture and to both Julie Maron and Rachida Barkani for their support during performance of experiments. Additionally, we would like to acknowledge Grégoire Harichaux and Jordan Goncalves, scientists at Bertin Pharma (part of Oncodesign, Orléans, France), for the determination of the P-gp expression levels using the MS2PLEX® technology.

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  1. Development Science, UCB Biopharma SRL, Chemin du Foriest, B1420, Braine-l’Alleud, Belgium

    Johan Nicolaï, Hélène Chapy, Eric Gillent, Kenneth Saunders, Anna-Lena Ungell, Jean-Marie Nicolas & Hugues Chanteux

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  1. Johan Nicolaï
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  2. Hélène Chapy
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Nicolaï, J., Chapy, H., Gillent, E. et al. Impact of In Vitro Passive Permeability in a P-gp-transfected LLC-PK1 Model on the Prediction of the Rat and Human Unbound Brain-to-Plasma Concentration Ratio. Pharm Res 37, 175 (2020). https://doi.org/10.1007/s11095-020-02867-z

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