Skip to main content
Log in

Nonlinear mixed effects modelling approach in investigating phenobarbital pharmacokinetic interactions in epileptic patients

  • Pharmacokinetics and Disposition
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

The present study aimed to establish population pharmacokinetic model for phenobarbital (PB), examining and quantifying the magnitude of PB interactions with other antiepileptic drugs concomitantly used and to demonstrate its use for individualization of PB dosing regimen in adult epileptic patients.

Methods

In total 205 PB concentrations were obtained during routine clinical monitoring of 136 adult epilepsy patients. PB steady state concentrations were measured by homogeneous enzyme immunoassay. Nonlinear mixed effects modelling (NONMEM) was applied for data analyses and evaluation of the final model.

Results

According to the final population model, significant determinant of apparent PB clearance (CL/F) was daily dose of concomitantly given valproic acid (VPA). Typical value of PB CL/F for final model was estimated at 0.314 l/h. Based on the final model, co-therapy with usual VPA dose of 1000 mg/day, resulted in PB CL/F average decrease of about 25 %, while 2000 mg/day leads to an average 50 % decrease in PB CL/F.

Conclusions

Developed population PB model may be used in estimating individual CL/F for adult epileptic patients and could be applied for individualizing dosing regimen taking into account dose-dependent effect of concomitantly given VPA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Yasiry Z, Shorvon SD (2012) How phenobarbital revolutionized epilepsy therapy: the story of phenobarbital therapy in epilepsy in the last 100 years. Epilepsia 53:26–39

    Article  CAS  PubMed  Google Scholar 

  2. Michelucci R, Pasini E, Tassinari CA (2009) Phenobarbital, primidone and other barbiturates. In: Shorvon S, Perucca E, Engel J (eds) The treatment of epilepsy, 3rd edn. Blackwell Publishing, Oxford, pp 585–603

    Chapter  Google Scholar 

  3. Summary of product characteristics for Phenobarbital available on: http://www.medicines.org.uk/emc/medicine/24077/SPC/Phenobarbital+Tablets+BP+30mg/.

  4. Nelson E, Powell JR, Conrad K, Likes K, Byers J, Baker S, Perrier D (1982) Phenobarbital pharmacokinetics and bioavailability in adults. J Clin Pharmacol 22(2–3):141–148

    Article  CAS  PubMed  Google Scholar 

  5. Vučićević K, Miljković B, Veličković R, Pokrajac M, Mrhar A, Grabnar I (2007) Population pharmacokinetic model of carbamazepine derived from routine therapeutic drug monitoring data. Ther Drug Monit 29(6):781–788

    Article  PubMed  Google Scholar 

  6. Klotz U (2007) The role of pharmacogenetics in the metabolism of antiepileptic drugs. pharmacokinetic and therapeutic implications. Clin Pharmacokinet 46(4):271–279

    Article  CAS  PubMed  Google Scholar 

  7. Mamiya K, Hadama A, Yukawa E, Ieiri I, Otsubo K, Ninomiya H, Tashiro N, Higuchi S (2000) CYP2C19 polymorphism effect on phenobarbitone. Pharmacokinetics in Japanese patients with epilepsy: analysis by population pharmacokinetics. Eur J Clin Pharmacol 55(11–12):821–825

    Article  CAS  PubMed  Google Scholar 

  8. Yukawa E, Mamiya K (2006) Effect of CYP2C19 genetic polymorphism on pharmacokinetics of phenytoin and phenobarbital in Japanese epileptic patients using non-linear mixed effects model approach. J Clin Pharm Ther 31(3):275–282

    Article  CAS  PubMed  Google Scholar 

  9. Landmark CJ, Johannessen SI, Tomson T (2012) Host factors affecting antiepileptic drug delivery-pharmacokinetic variability. Adv Drug Deliver Rev 64(10):896–910

    Article  Google Scholar 

  10. Patsalos PN, Perucca E (2003) Clinically important drug interactions in epilepsy: general features and interactions between antiepileptic drugs. Lancet Neurol 2(6):347–356

    Article  CAS  PubMed  Google Scholar 

  11. British Medical Association and the Royal Pharmaceutical Society of Great Britain. British National Formulary. 67th ed. UK: BMJ Publishing Group. March 2014.

  12. Yukawa E, To H, Ohdo S, Higuchi S, Aoyama T (1998) Detection of a drug-drug interaction on population-based phenobarbitone clearance using nonlinear mixed-effects modeling. Eur J Clin Pharmacol 54(1):69–74

    Article  CAS  PubMed  Google Scholar 

  13. Jovanović M, Sokić D, Grabnar I, Vovk T, Prostran M, Vučićević K, Miljković B (2013) Population pharmacokinetics of topiramate in adult patients with epilepsy using nonlinear mixed effects modelling. Eur J Pharm Sci 50(3–4):282–289

  14. Perucca E (2006) Clinically relevant drug interactions with antiepileptic drugs. Br J Clin Pharmacol 61(3):246–255

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Patsalos PN, Froscher W, Pisani F, van Rijn CM (2002) The importance of drug interactions in epilepsy therapy. Epilepsia 43(4):365–385

    Article  CAS  PubMed  Google Scholar 

  16. Yukawa M, Yukawa E, Suematsu F, Takiguchi T, Ikeda H, Aki H, Mimemoto M (2011) Population pharmacokinetics of phenobarbital by mixed effect modelling using routine clinical pharmacokinetic data in Japanese neonates and infants: an update. J Clin Pharm Ther 36(6):704–710

    Article  CAS  PubMed  Google Scholar 

  17. Beal SL, Sheiner LB, Boeckmann AJ (1989–2011) NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA

  18. Bauer RJ (2011) NONMEM Users Guides. Introduction to NONMEM 7.2.0. Icon Development Solutions Ellicott City, Maryland, USA.

  19. Jonsson EN, Karlsson MO (1998) Automated covariate model building within NONMEM. Pharm Res 15(9):1463–1468

    Article  CAS  PubMed  Google Scholar 

  20. Hooker AC, Staatz CE, Karlsson MO (2007) Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method. Pharm Res 24(12):2187–2197

    Article  CAS  PubMed  Google Scholar 

  21. Karlsson MO, Savic RM (2007) Diagnosing model diagnostics. Clin Pharmacol Ther 82(1):17–20

    Article  CAS  PubMed  Google Scholar 

  22. Parke J, Holford NH, Charles BG (1999) A procedure for generating bootstrap samples for the validation of nonlinear mixed-effects population models. Comput Methods Programs Biomed 59(1):19–29

    Article  CAS  PubMed  Google Scholar 

  23. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO (2011) Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. Aaps J 13:143–151

    Article  PubMed Central  PubMed  Google Scholar 

  24. Booth BP, Gobburu JV (2003) Considerations in analyzing single-trough samples using mixed-effect modeling. J Clin Pharmacol 43(12):1307–1315

    Article  PubMed  Google Scholar 

  25. Ahn JE, Birnbaum AK, Brundage RC (2005) Inherent correlation between dose and clearance in therapeutic drug monitoring settings: possible misinterpretation in population pharmacokinetic analyses. J Pharmacokinet Phar 32(5–6):703–718

    Article  Google Scholar 

  26. Vučićević K, Miljković B, Pokrajac M, Prostran M, Martinović Ž, Grabnar I (2009) The influence of drug-drug interaction and patients’ characteristics on valproic acid’s clearance in adults with epilepsy using nonlinear mixed effects modeling. Eur J Pharm Sci 38(5):512–518

    Article  PubMed  Google Scholar 

  27. Botha JH, Gray ARM (1995) Determination of phenobarbitone population clearance values for South African children. Eur J Clin Pharmacol 48(5):381–383

    Article  CAS  PubMed  Google Scholar 

  28. Spina E, Pisani F, Perucca E (1996) Clinically significant pharmacokinetic drug interactions with carbamazepine—an update. Clin Pharmacokin 31(3):198–214

    Article  CAS  Google Scholar 

  29. Kapetanovic IM, Kupferberg HJ, Porter RJ, Theodore W, Schulman E, Penry JK (1981) Mechanism of valproate-phenobarbital interaction in epileptic patients. Clin Pharmacol Ther 29:480–486

    Article  CAS  PubMed  Google Scholar 

  30. Bernus I, Dickinson RG, Hooper WD, Eadie MJ (1994) Inhibition of phenobarbitone N-glucosidation by valproate. Br J Clin Pharmacol 38(5):411–416

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Wilder BJ, Willmore LJ, Bruni J, Villarreal HJ (1978) Valproic acid: interaction with other anticonvulsant drugs. Neurology 28(9 Pt 1):892–896

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was conducted as a part of the project Experimental and Clinical Pharmacological Investigations of Mechanisms of Drug Action and Interactions in Nervous and Cardiovascular System (No. 175023) funded by the Ministry of Education, Science and Technological Development, Belgrade, Republic of Serbia. We would like to acknowledge Prof. Milena Pokrajac for her significant contribution to the research. We are very grateful to the personnel from the Institute of Mental Health, Belgrade. Additionally, the authors would like to express great appreciation to our colleague Spec Clin Pharm Ružica Veličković for her valuable role in collecting the data.

Conflict of interest

None.

Author Contributions

Ž.M., B.M., M.P. designed the study. B.M., Ž.M. and K.V. performed the trial. K.V. gathered the data. K.V., M.J. and B.G. performed the analysis. B.M., K.V., S.V.K., B.G. and M.J. interpreted the results and wrote the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katarina Vučićević.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOC 39 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vučićević, K., Jovanović, M., Golubović, B. et al. Nonlinear mixed effects modelling approach in investigating phenobarbital pharmacokinetic interactions in epileptic patients. Eur J Clin Pharmacol 71, 183–190 (2015). https://doi.org/10.1007/s00228-014-1778-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00228-014-1778-7

Keywords

Navigation