Compliance spectrum as a drug fingerprint of drug intake and drug disposition

  • Olivier Barrière
  • Jun Li
  • Fahima Nekka
Original Paper


Since drug related variability arises from different origins, particularly driven by the behaviour or physiology of the patient, the problems of drug intake and drug disposition are separately presented in general. To overcome the potential drawbacks of this artificial split, we propose in this paper a combined illustrative approach, named compliance spectrum, such that these two subprocesses can be equitably studied and visualized. We construct the compliance spectrum based on the Bayesian decision method we previously developed for the inverse problem of patient compliance within the framework of Population-PK. This spectrum provides an intuitive and interactive way to evaluate the relationship between drug intake and drug disposition along with their consequences on PK profile. As well, it opens a new direction for model quality diagnostic.


Compliance Pharmacokinetics  Modelling Inverse problem Bayesian decision theory Visual representation 



This work has been supported by Mprime, FQRNT and NSERC. The Centre de Recherches Mathématiques and Faculty of Pharmacy of Université de Montréal are also acknowledged for their support. Special thanks for Professor Alain Arnéodo for his valuable comments and suggestions.


  1. 1.
    World Health Organization (2003) Adherence to long-term therapies: evidence for action,
  2. 2.
    Harter JG, Peck CC (1991) Chronobiology suggestions for integrating it into drug development, Ann N Y Acad Sci 618:563–571PubMedCrossRefGoogle Scholar
  3. 3.
    Urquhart J (2000) Erratic patient compliance with prescribed drug regimens: target for drug. Clin Pharmacol Ther 67(4):331–334PubMedCrossRefGoogle Scholar
  4. 4.
    Macilwain Colin (1997) Better adherence vital in AIDS therapies. Nature 390:326PubMedCrossRefGoogle Scholar
  5. 5.
    Blaschke TF, Osterberg L, Vrijens B, Urquhart J (2012) Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu Rev Pharmacol Toxicol 52:275–301PubMedCrossRefGoogle Scholar
  6. 6.
    Kenna LA, Sheiner LB (2004) Estimating treatment effect in the presence of non-compliance measured with error: precision and robustness of data analysis methods. Stat Med 23(23):3561–80PubMedCrossRefGoogle Scholar
  7. 7.
    Mu S, Ludden TM (2003) Estimation of population pharmacokinetic parameters in the presence of non-compliance. J Pharmacokinet Pharmacodyn 30(1):53–81PubMedCrossRefGoogle Scholar
  8. 8.
    Vrijens B, Tousset E, Rode R, Bertz R, Mayer S, Urquhart J (2005) Successful projection of the time course of drug concentration in plasma during a 1-Year period from electronically compiled dosing-time data used as input to individually parameterized pharmacokinetic models. J Clin Pharmacol 45:461PubMedCrossRefGoogle Scholar
  9. 9.
    Vrijens B, Goetghebeur E, de Klerk E, Rode R, Mayer S, Urquhart J (2005) Modelling the association between adherence and viral load in HIV-infected patients. Stat Med 24:2719–2731PubMedCrossRefGoogle Scholar
  10. 10.
    Girard P, Blaschke TF, Kastrissios H et al. (1998) A Markov mixed effect regression model for drug compliance. Stat Med 17:2313–2333PubMedCrossRefGoogle Scholar
  11. 11.
    Wang W, Husan F, Chow S (1996) The impact of patient compliance on drug concentration profile in multiple doses. Stat Med 15(6):659–669PubMedCrossRefGoogle Scholar
  12. 12.
    Li J, Nekka F (2007) A pharmacokinetic formalism explicitly integrating the patient drug compliance. J Pharmacokinet Pharmacodyn 34(1):115–39PubMedCrossRefGoogle Scholar
  13. 13.
    Li J, Nekka F (2009) A probabilistic approach for the evaluation of pharmacological effect induced by patient irregular drug intake. J Pharmacokinet Pharmacodyn 36(3):221–46PubMedCrossRefGoogle Scholar
  14. 14.
    Sarem S, Li J, Nekka F (2011) Compliance descriptors: analysis and evaluation in terms of therapeutic effect. Biopharm Drug Dispos 32(2):76–88PubMedCrossRefGoogle Scholar
  15. 15.
    Barriére O, Li J, Nekka F (2011) A Bayesian approach for the estimation of patient compliance based on the last sampling information. J pharmacokinet pharmacodyn 38(3):333–51PubMedCrossRefGoogle Scholar
  16. 16.
    Rubio A, Cox C, Weintraub M (1992) Prediction of diltiazem plasma concentration curves from limited measurements using compliance data. Clin pharmacokinet 22(3):238–246PubMedCrossRefGoogle Scholar
  17. 17.
    Widmer N, Decosterd LA, Csajka C, Leyvraz S, Duchosal MA, Rosselet A, Rochat B, Eap CB, Henry H, Biollaz J, Buclin T (2006) Population pharmacokinetics of imatinib and the role of alpha-acid glycoprotein. Br J Clin Pharmacol 62(1):97–112PubMedCrossRefGoogle Scholar
  18. 18.
    Feng Y, Gastonguay MR, Pollock BG, Frank E, Kepple GH, Bies RR (2011) Performance of Cpred/Cobs concentration ratios as a metric reflecting adherence to antidepressant drug therapy. Neuropsychiatr Dis Treat 7:117–25PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Faculté de PharmacieUniversité de Montréal C.P. 6128MontréalCanada
  2. 2.Centre de recherches mathématiquesUniversité de Montréal C.P. 6128MontréalCanada
  3. 3.Centre for Applied Mathematics in Bioscience and MedicineMcGill University 3655 Promenade Sir William OslerMontréalCanada

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