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Physiologically Based Pharmacokinetic (PBPK) Modeling of Everolimus (RAD001) in Rats Involving Non-Linear Tissue Uptake

  • Robert Laplanche
  • Guy M. L. Meno-TetangEmail author
  • Ryosei Kawai
Article

Everolimus is a novel macrolide immunosuppressant developed for the prophylaxis of allogeneic renal or cardiac transplant rejection. Treatments with immunosuppressants are often associated with organ toxicity that is linked to high organ exposure. Therefore, gaining insight into the pharmacokinetics of everolimus in various organs is highly desirable especially those organs of therapeutic interest or those that pose safety concerns. The aim of this work was to characterize the disposition kinetics of everolimus in rats by physiologically based pharmacokinetic (PBPK) modeling.

Blood and tissue samples were collected from male Wistar rats over 24 hr following intravenous (iv) bolus and iv infusion of 1 mg/kg and 10 mg/kg/2 hr of everolimus. Further blood samples were collected between 1 and 170 hr from a third group of rats, which received iv infusion of 1 mg/kg/2 hr of everolimus. Drug concentrations in blood and tissues were determined by a liquid chromatography reverse dilution method. Distribution of everolimus between blood fractions was determined in vitro at 37°C.

The results of the study demonstrated that everolimus exhibited moderate non-linear binding to red blood cells. Also, the tissue-to-blood concentration ratio decreased in all tissues as blood concentration increased. A PBPK model involving non-linear tissue binding was able to successfully describe the observed data in blood and all the organs investigated. The highest binding potential was observed in thymus, lungs, and spleen with the greatest tissue affinity observed in thymus, skin, and muscle as compared to other tissues. Everolimus exhibited a high clearance rate that was limited to the hepatic blood flow (47.2 ml/min/kg). The PBPK model was also able to predict the venous blood concentration reasonably well following oral administration. The oral bioavailability value, as estimated with the PBPK, was 12% and was similar to the value obtained by non-compartmental analysis.

In conclusion, A PBPK model has been developed that successfully predicts the time course of everolimus in blood and a variety of organs. This model takes into account the non- linear binding of everolimus to red blood cells and tissues. This model may be used to predict everolimus concentration–time course in organs from other species including humans.

Keywords

PBPK non-linear binding tissue distribution everolimus RAD001 

Abbreviations

Aas, Aad

amount of labeled analyte in samples, amount of analyte detected

Acs, Acd

amount of non-labeled carrier in samples, amount of carrier detected

Adepot

amount of absorbable drug in the “dummy” oral depot compartment

CB,i

concentration in venous blood leaving organ “i

CBA, CBV

arterial and venous blood concentrations

CBC

concentration in blood cells

CL, CLint

total and intrinsic clearance

CP, Ci

concentration in plasma and in organ “i

CsA

cyclosporine A

Cu, Cu,i

unbound concentration in blood and in organ “i

Fabs

fraction of dose absorbed

FP, FBC

fraction of drug in plasma and in red blood cells

fu

free unbound fraction in plasma

H

hematocrit

kabs

first-order rate constant for drug absorption

kel

first-order elimination rate constant

KdBC, Kdi

dissociation constant in blood cells and in tissue “i

kdeg

first-order rate constant for degradation in plasma

Km, Vm

Michaelis–Menten constant and maximum velocity

NPBC, NPi

binding capacity of blood cells and of tissue “i

NSBC, NSi

non-saturable fraction in blood cells and in tissue “i

Qhepatic

venous hepatic blood flow

Qi, Vi

blood flow to organ “i” and organ volume

Rinf

zero-order intravenous infusion rate

Vc

volume of the central compartment

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References

  1. 1.
    Abraham R.T., Wiederrecht G.J. (1996) Immunopharmacology of rapamycin. Ann. Rev. Immunol. 14:483–510CrossRefGoogle Scholar
  2. 2.
    S. N. Sehgal. Immunosuppressive profile of rapamycin. In C. Allison, K. J. Lafferty, and H. Fliri (eds.) Immunosuppressive and Anti-inflammatory Drugs. The New York Academy of Sciences, New York. Ann. N. Y. Acad. Sci. 696:1–8 (1993).Google Scholar
  3. 3.
    Waldmann T.A., Tagaya Y. (1999) The multifaceted regulation of interleukin-15 expression and the role of this cytokine in NK cell differentiation and host response to intracellular pathogens. Ann. Rev Immunol. 17:19–49CrossRefGoogle Scholar
  4. 4.
    Baan C.C., Knoop C.J., van Gelder T., Holweg C.T., Niesters H.G., Smeets T.J., van der Ham F., Zondervan P.E., Maat L.P., Balk A.H., Weimar W. (1999) Anti-CD25 therapy reveals the redundancy of the intragraft cytokine network after clinical heart transplantation. Transplantation 67:870–876PubMedCrossRefGoogle Scholar
  5. 5.
    Cole O.J., Shehata M., Rigg K.M. (1998) Effect of SDZ RAD on transplant arteriosclerosis in the rat aortic model. Transplant. Proc. 30:2200–2203Google Scholar
  6. 6.
    Hausen B., Ikonen T., Briffa N. (2000) Combined immunosuppression with cyclosporine (Neoral) and SDZ RAD in non-human primate lung transplantation: systematic pharmacokinetic-based trials to improve efficacy and tolerability. Transplantation 69:76–86PubMedCrossRefGoogle Scholar
  7. 7.
    Schuurman H.J., Ringers J., Schuler W. (2000) Oral efficacy of the macrolide immunosuppressant SDZ RAD and of cyclosporine microemulsion in cynomolgus monkey kidney allotransplantation. Transplantation 69:737–742PubMedCrossRefGoogle Scholar
  8. 8.
    Jacobsen W., Serkova N., Hallensleben K. (1999) In vitro metabolism of the novel immunosuppressant SDZ-RAD and comparison with rapamycin. ISSX Proc. 15:24–28Google Scholar
  9. 9.
    W. Jacobsen, N. Serkova, B. Hausen, R. E. Morris, L. Z. Benet, and U. Christians. Comparison of the in vitro metabolism of the macrolide immunosuppressants Sirolimus and RAD. Transplant. Proc. 33:514–515 (2001).PubMedCrossRefGoogle Scholar
  10. 10.
    C. Vidal, G. I. Kirchner, and K.-F. Sewing. Structural elucidation by electrospray mass spectrometry: an approach to the in vitro metabolism of the macrolide immunosuppressant SDZ RAD. J. Am. Soc. Mass Spectrom. 9:1267–1274 (1998).Google Scholar
  11. 11.
    R. Kawai, M. Lemaire, J. L. Steimer, A. Bruelisauer, W. Niederberger, and M. Rowland. Physiologically based pharmacokinetic study on a cyclosporin derivative, SDZ IMM 125. J. Pharmacokinet. Biopharm. 22:327–365 (1994).Google Scholar
  12. 12.
    R. Kawai, D. Mathew, C. Tanaka, and M. Rowland. Physiologically based pharmacokinetics of cyclosporine A: extension to tissue distribution kinetics in rats and scale-up to human. J. Pharmacol. Exp. Ther. 287(2):457 (1998).Google Scholar
  13. 13.
    Tanaka C., Kawai R., Rowland M. (1999) Physiologically based pharmacokinetics of cyclosporine A: reevaluation of dose-nonlinear kinetics in rats. J. Pharmacokinet. Biopharm. 27(6):597–623PubMedCrossRefGoogle Scholar
  14. 14.
    Urien S., Bastian G., Lucas C., Bizzari J.P., Tillement J.P. (1992) Binding of a new vinca alcaloid derivative, S12363, to human plasma proteins and platelets. Usefulness of an erythrocyte partitioning technique. Invest. New Drugs 10:263–268CrossRefGoogle Scholar
  15. 15.
    Crowe A., Bruelishauer A., Duerr L., Guntz P., Lemaire M. (1999) Absorption and intestinal metabolism of SDZ RAD and Rapamycin in rats. Drug Metab. Dispos. 27:627–632PubMedGoogle Scholar
  16. 16.
    D. Z. D’Argenio and A. Schumitzky ADAPT II User’s Guide: Pharmacokinetic/Pharmacodynamic Systems Analysis Software. Biomedical Simulations Resources at University of Southern California, Los Angeles (1997).Google Scholar
  17. 17.
    Simusolv on VAX/VMS, Version 3. Dow Chemical Company, Midland Michigan.Google Scholar
  18. 18.
    SAAM II Software applications for kinetic analysis, Version 1.2.1. SAAM II Institute, University of Washington.Google Scholar
  19. 19.
    Bernareggi A., Rowland M. (1991) Physiologic modeling of cyclosporin kinetics in rats and man. J. Pharmacokinet. Biopharm. 19:21–50PubMedCrossRefGoogle Scholar
  20. 20.
    Reinoso R.F., Telfer B.A., Rowland M. (1997) Tissue water content in rats measured by desiccation. J. Pharmacol. Toxicol. Methods. 38(2):87–92PubMedCrossRefGoogle Scholar
  21. 21.
    Crowe A., Lemaire M. (1998) In vitro and in situ absorption of SDZ-RAD using a human intestinal cell line (Caco-2) and a single pass perfusion model in rats: comparison with rapamycin. Pharm. Res. 15:1666–1672PubMedCrossRefGoogle Scholar
  22. 22.
    A. Tsuji, T. Yoshikawa, K. Nishide, H. Minami, M. Kimura, E. Nakashima, T. Terasaki, E. Miyamoto, C.H. Nightingale, and T. Yamana. Physiologically based pharmacokinetic model for beta-lactam antibiotics I: tissue distribution and elimination in rats. J. Pharm. Sci. 72:1239–1252 (1983).Google Scholar
  23. 23.
    Ferron G.M., Jusko W.J. (1998) Species differences in sirolimus stability in humans, rabbits, and rats. Drug Metab. Dispos. 26:83–84PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Robert Laplanche
    • 1
  • Guy M. L. Meno-Tetang
    • 2
    Email author
  • Ryosei Kawai
    • 3
  1. 1.Biomarker DevelopmentNovartis Pharma AGBaselSwitzerland
  2. 2.Merck Serono International SAGeneveSwitzerland
  3. 3.Exploratory DevelopmentNovartis PharmaceuticalsTokyoJapan

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