Skip to main content
SpringerLink
Log in

A Bayesian approach for the estimation of patient compliance based on the last sampling information

  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

Poor adherence to a drug prescription significantly impacts on the efficacy and safety of a planned therapy. The relationship between drug intake and pharmacokinetics (PK) is only partially known. In this work, we focus on the so-called “inverse problem”, concerned with the issue of retracing the patient compliance scenario using limited clinical knowledge. Using a reported Pop-PK model of imatinib, and accounting for the variability around its PK parameters, we were able to simulate a whole range of drug concentration values at a specific sampling point for a population of patients with all possible drug compliance profiles. Using a Bayesian decision rule, we developed a methodology for the determination of the associated compliance profile prior to a given sampling value. The adopted approach allows, for the first time, to quantitatively acquire knowledge about the compliance patterns having a causal effect on a given PK. Moreover, using a simulation approach, we were able to evaluate the evolution of success rate of the retracing process in terms of the considered time period before sampling as well as the model-inherited variability. In conclusion, this work allows, from a probability viewpoint, to propose a solution for this inverse problem of compliance determination.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M (2008) Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ 336(7653):1114–1119

    Article  PubMed  Google Scholar 

  2. Urquhart J (2000) Erratic patient compliance with prescribed drug regimens: target for drug. Clin Pharmacol Ther 67(4):331–334

    Article  PubMed  CAS  Google Scholar 

  3. Hénin E, Tod M, Trillet-Lenoir V, Rioufol C, Tranchand B, Girard P (2009) Pharmacokinetically based estimation of patient compliance with oral anticancer chemotherapies: in silico evaluation. Clin Pharmacokinet 48(6):359–369

    Article  PubMed  Google Scholar 

  4. Li J, Nekka F (2007) A pharmacokinetic formalism explicitly integrating the patient drug compliance. J Pharmacokinet Pharmacodyn 34(1):115–139

    Article  PubMed  Google Scholar 

  5. Mu S, Ludden TM (2003) Estimation of population harmacokinetic parameters in the presence of non-compliance. JPKPD 30(1):53–81

    Google Scholar 

  6. Soy D, Beal SL, Sheiner LB (2004) Population one-compartment pharmacokinetic analysis with missing dosage data. Clin Pharmacol Ther 76(5):441–451

    Article  PubMed  CAS  Google Scholar 

  7. Jonsson EN, Wade JR, Almqvist G et al (1997) Discrimination between rival dosing histories. Pharm Res 14(8):984–991

    Article  PubMed  CAS  Google Scholar 

  8. Girard P, Sheiner L, Kastrissios H, Blaschke T (1996) Do we need full compliance data for population pharmacokinetic analysis?. J Pharmacokinet Biopharm 24(3):265–282

    Article  PubMed  CAS  Google Scholar 

  9. Vrijens B, Goetghebeur E (2004) Electronic monitoring of variation in drug intakes can reduce bias and improve precision in pharmacokinetic/pharmacodynamic population studies. Stat Med 23(4):531–544

    Article  PubMed  Google Scholar 

  10. 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):221–246

    Article  PubMed  Google Scholar 

  11. 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):97–112

    Article  PubMed  CAS  Google Scholar 

  12. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

Download references

Acknowledgment

This work has been supported by FQRNT, NSERC and MITACS. The Centre de Recherches Mathématiques of Université de Montréal is also acknowledged for its support.

Author information

Authors and Affiliations

  1. Faculté de Pharmacie, Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada

    Olivier Barrière, Jun Li & Fahima Nekka

  2. Centre de recherches mathématiques, Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, QC, H3C 3J7, Canada

    Jun Li & Fahima Nekka

Authors
  1. Olivier Barrière
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fahima Nekka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fahima Nekka.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barrière, O., Li, J. & Nekka, F. A Bayesian approach for the estimation of patient compliance based on the last sampling information. J Pharmacokinet Pharmacodyn 38, 333–351 (2011). https://doi.org/10.1007/s10928-011-9196-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-011-9196-2

Keywords

Search

Navigation