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01.03.2012 | Original Research Article | Ausgabe 3/2012

Clinical Pharmacokinetics 3/2012

Therapeutic Drug Monitoring of Imatinib

Bayesian and Alternative Methods to Predict Trough Levels

Clinical Pharmacokinetics > Ausgabe 3/2012
Verena Gotta, Nicolas Widmer, Michael Montemurro, Serge Leyvraz, Amina Haouala, Laurent A. Decosterd, Chantal Csajka, Dr Thierry Buclin
Wichtige Hinweise

Electronic Supplementary Material

Supplementary material is available for this article at 10.​2165/​11596990-000000000-00000 and is accessible for authorized users.



The imatinib trough plasma concentration (Cmin) correlates with clinical response in cancer patients. Therapeutic drug monitoring (TDM) of plasma Cmin is therefore suggested. In practice, however, blood sampling for TDM is often not performed at trough. The corresponding measurement is thus only remotely informative about Cmin exposure.


The objectives of this study were to improve the interpretation of randomly measured concentrations by using a Bayesian approach for the prediction of Cmin, incorporating correlation between pharmacokinetic parameters, and to compare the predictive performance of this method with alternative approaches, by comparing predictions with actual measured trough levels, and with predictions obtained by a reference method, respectively.


A Bayesian maximum a posteriori (MAP) estimation method accounting for correlation (MAP-ρ) between pharmacokinetic parameters was developed on the basis of a population pharmacokinetic model, which was validated on external data. Thirty-one paired random and trough levels, observed in gastrointestinal stromal tumour patients, were then used for the evaluation of the Bayesian MAP-ρ method: individual Cmin predictions, derived from single random observations, were compared with actual measured trough levels for assessment of predictive performance (accuracy and precision). The method was also compared with alternative approaches: classical Bayesian MAP estimation assuming uncorrelated pharmacokinetic parameters, linear extrapolation along the typical elimination constant of imatinib, and non-linear mixed-effects modelling (NONMEM) first-order conditional estimation (FOCE) with interaction. Predictions of all methods were finally compared with ‘best-possible’ predictions obtained by a reference method (NONMEM FOCE, using both random and trough observations for individual Cmin prediction).


The developed Bayesian MAP-ρ method accounting for correlation between pharmacokinetic parameters allowed non-biased prediction of imatinib Cmin with a precision of ±30.7%. This predictive performance was similar for the alternative methods that were applied. The range of relative prediction errors was, however, smallest for the Bayesian MAP-ρ method and largest for the linear extrapolation method. When compared with the reference method, predictive performance was comparable for all methods. The time interval between random and trough sampling did not influence the precision of Bayesian MAP-ρ predictions.


Clinical interpretation of randomly measured imatinib plasma concentrations can be assisted by Bayesian TDM. Classical Bayesian MAP estimation can be applied even without consideration of the correlation between pharmacokinetic parameters. Individual Cmin predictions are expected to vary less through Bayesian TDM than linear extrapolation. Bayesian TDM could be developed in the future for other targeted anticancer drugs and for the prediction of other pharmacokinetic parameters that have been correlated with clinical outcomes.

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