Advertisement

Pharmaceutical Research

, Volume 31, Issue 10, pp 2605–2617 | Cite as

Dopamine D2 Receptor Occupancy as a Predictor of Catalepsy in Rats: A Pharmacokinetic-Pharmacodynamic Modeling Approach

  • Martin Johnson
  • Magdalena Kozielska
  • Venkatesh Pilla Reddy
  • An Vermeulen
  • Hugh A. Barton
  • Sarah Grimwood
  • Rik de Greef
  • Geny M. M. Groothuis
  • Meindert Danhof
  • Johannes H. Proost
Research Paper

Abstract

Objectives

Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans.

Methods

Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT.

Results

Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats.

Conclusion

A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

KEY WORDS

catalepsy dopamine D2 receptor antagonist EPS Markov model schizophrenia 

Notes

ACKNOWLEDGMENTS

This research article was prepared within the framework of project no. D2-104 of the Dutch Top Institute Pharma (Leiden, The Netherlands; www.tipharma.com). The authors acknowledge Dr. Megens from Janssen Research and Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium, for the valuable discussion on animal pharmacology models. The authors have no conflicts of interest that are directly relevant to the contents of this research article.

REFERENCES

  1. 1.
    de Greef R, Maloney A, Olsson-Gisleskog P, Schoemaker J, Panagides J. Dopamine D(2) occupancy as a biomarker for antipsychotics: quantifying the relationship with efficacy and extrapyramidal symptoms. AAPS J. 2011;13(1):121–30.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Kapur S, Remington G, Jones C, Wilson A, DaSilva J, Houle S, et al. High levels of dopamine D-2 receptor occupancy with low-dose haloperidol treatment: a PET study. Am J Psychiatry. 1996;153(7):948–50.PubMedGoogle Scholar
  3. 3.
    Farde L, Nordstrom AL, Wiesel FA, Pauli S, Halldin C, Sedvall G. Positron emission tomographic analysis of central D1-dopamine and D2-dopamine receptor occupancy in patients treated with classical neuroleptics and clozapine - relation to extrapyramidal side-effects. Arch Gen Psychiatry. 1992;49(7):538–44.PubMedCrossRefGoogle Scholar
  4. 4.
    Nordstrom AL, Farde L, Wiesel FA, Forslund K, Pauli S, Halldin C, et al. Central D2-dopamine receptor occupancy in relation to antipsychotic drug effects - a double-blind pet study of schizophrenic-patients. Biol Psychiatry. 1993;33(4):227–35.PubMedCrossRefGoogle Scholar
  5. 5.
    Horacek J, Bubenikova-Valesova V, Kopecek M, Palenicek T, Dockery C, Mohr P, et al. Mechanism of action of atypical antipsychotic drugs and the neurobiology of schizophrenia. CNS Drugs. 2006;20(5):389–409.PubMedCrossRefGoogle Scholar
  6. 6.
    Hoffman DC, Donovan H. Catalepsy as a rodent model for detecting antipsychotic-drugs with extrapyramidal side-effect liability. Psychopharmacology (Berl). 1995;120(2):128–33.CrossRefGoogle Scholar
  7. 7.
    Wadenberg MLG, Kapur S, Soliman A, Jones C, Vaccarino F. Dopamine D-2 receptor occupancy predicts catalepsy and the suppression of conditioned avoidance response behavior in rats. Psychopharmacology (Berl). 2000;150(4):422–9.CrossRefGoogle Scholar
  8. 8.
    Mager DE, Jusko WJ. Development of translational pharmacokinetic-pharmacodynamic models. Clin Pharmacol Ther. 2008;83(6):909–12.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Yassen A, Olofsen E, Kan J, Dahan A, Danhof M. Animal-to-human extrapolation of the pharmacokinetic and pharmacodynamic properties of buprenorphine. Clin Pharmacokinet. 2007;46(5):433–47.PubMedCrossRefGoogle Scholar
  10. 10.
    Danhof M, De Lange ECM, Della Pasqua OE, Ploeger BA, Voskuyl RA. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends Pharmacol Sci. 2008;29(4):186–91.PubMedCrossRefGoogle Scholar
  11. 11.
    Zuideveld KP, Van der Graaf PH, Peletier LA, Danhof M. Allometric scaling of pharmacodynamic responses: application to 5-Ht1A receptor mediated responses from rat to man. Pharm Res. 2007;24(11):2031–9.PubMedCrossRefGoogle Scholar
  12. 12.
    Bonate PL. Principles of simulation. Pharmacokinetic-pharmacodynamic modeling and simulation. Springer; 2011. p. 489.Google Scholar
  13. 13.
    Janssen PAJ, Niemegeers CJE, Schellekens KH. Is it possible to predict the clinical effects of neuroleptic drugs (major tranquilizers) from animal data? Part I. Neuroleptic activity spectra for rats. Arzneim Forsch. 1965;15:104–17.Google Scholar
  14. 14.
    Johnson M, Kozielska M, Pilla Reddy V, Vermeulen A, Li C, Grimwood S, et al. Mechanism-based pharmacokinetic-pharmacodynamic modeling of the dopamine D(2) receptor occupancy of olanzapine in rats. Pharm Res. 2011;28(10):2490–504.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Kozielska M, Johnson M, Pilla Reddy V, Vermeulen A, Li C, Grimwood S, et al. Pharmacokinetic-pharmacodynamic modeling of the D2 and 5-HT2A receptor occupancy of risperidone and paliperidone in rats. Pharm Res. 2012;29(7):1932–48.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Parker TJ, Della Pasqua OE, Loizillon E, Chezaubernard C, Jochemsen R, Danhof M. Pharmacokinetic-pharmacodynamic modelling in the early development phase of anti-psychotics: a comparison of the effects of clozapine, S 16924 and S 18327 in the EEG model in rats. Br J Pharmacol. 2001;132(1):151–8.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Olsen CK, Brennum LT, Kreilgaard M. Using pharmacokinetic-pharmacodynamic modelling as a tool for prediction of therapeutic effective plasma levels of antipsychotics. Eur J Pharmacol. 2008;584(2–3):318–27.PubMedCrossRefGoogle Scholar
  18. 18.
    Dayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm. 1993;21(4):457–78.PubMedCrossRefGoogle Scholar
  19. 19.
    Ito K, Hutmacher MM, Liu J, Qiu R, Frame B, Miller R. Exposure-response analysis for spontaneously reported dizziness in pregabalin-treated patient with generalized anxiety disorder. Clin Pharmacol Ther. 2008;84(1):127–35.PubMedCrossRefGoogle Scholar
  20. 20.
    Sheiner LB, Beal SL, Dunne A. Analysis of nonrandomly censored ordered categorical longitudinal data from analgesic trials. J Am Stat Assoc. 1997;92(440):1235–44.CrossRefGoogle Scholar
  21. 21.
    Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides (1989-2009). Ellicott City: Icon Development Solutions; 2009.Google Scholar
  22. 22.
    R Development Core Team. R: a language and environment for statistical computing. Vienna: R Fundation for Statistical Computing; 2009.Google Scholar
  23. 23.
    Lindbom L, Pihlgren P, Jonsson EN. PsN-Toolkit–a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005;79(3):241–57.PubMedCrossRefGoogle Scholar
  24. 24.
    Pilla Reddy V, Petersson KJ, Suleiman AA, Vermeulen A, Proost JH, Friberg LE. Pharmacokinetic-pharmacodynamic modeling of severity levels of extrapyramidal side effects with Markov property. CPT: Pharmacometrics Syst Pharmacol. 2012;1:e1. doi: 10.1038/psp.2012.9.Google Scholar
  25. 25.
    Zingmark PH, Kagedal M, Karlsson MO. Modelling a spontaneously reported side effect by use of a Markov mixed-effects model. J Pharmacokinet Pharmacodyn. 2005;32(2):261–81.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Martin Johnson
    • 1
  • Magdalena Kozielska
    • 1
  • Venkatesh Pilla Reddy
    • 1
  • An Vermeulen
    • 2
  • Hugh A. Barton
    • 3
  • Sarah Grimwood
    • 3
  • Rik de Greef
    • 4
  • Geny M. M. Groothuis
    • 1
  • Meindert Danhof
    • 5
  • Johannes H. Proost
    • 1
  1. 1.Division of PharmacokineticsToxicology and Targeting, University of Groningen, University Centre for PharmacyGroningenThe Netherlands
  2. 2.Advanced PK-PD Modeling and Simulation, Janssen Research and Development, A Division of Janssen Pharmaceutica NVBeerseBelgium
  3. 3.Worldwide Research & Development Pfizer Inc.GrotonUSA
  4. 4.Pharmacokinetics, Pharmacodynamics & Drug Metabolism Merck Sharp & DohmeOssThe Netherlands
  5. 5.Division of PharmacologyLeiden Academic Center for Drug ResearchLeidenThe Netherlands

Personalised recommendations