title: Therapeutic drug monitoring of imatinib: Bayesian and alternative methods to predict trough levels.
abstract: BACKGROUND
  The imatinib trough plasma concentration (C(min)) correlates with clinical response in cancer patients. Therapeutic drug monitoring (TDM) of plasma C(min) 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 C(min) exposure.
  
  
  OBJECTIVE
  The objectives of this study were to improve the interpretation of randomly measured concentrations by using a Bayesian approach for the prediction of C(min), 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.
  
  
  METHODS
  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 C(min) 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 C(min) prediction).
  
  
  RESULTS
  The developed Bayesian MAP-ρ method accounting for correlation between pharmacokinetic parameters allowed non-biased prediction of imatinib C(min) 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.
  
  
  CONCLUSIONS
  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 C(min) 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.
authors.affiliation: Division of Clinical Pharmacology and Toxicology, Laboratory Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
authors.forename: Verena
authors.initials: V
authors.lastname: Gotta
authors.forename: Nicolas
authors.initials: N
authors.lastname: Widmer
authors.forename: Michael
authors.initials: M
authors.lastname: Montemurro
authors.forename: Serge
authors.initials: S
authors.lastname: Leyvraz
authors.forename: Amina
authors.initials: A
authors.lastname: Haouala
authors.forename: Laurent A
authors.initials: LA
authors.lastname: Decosterd
authors.forename: Chantal
authors.initials: C
authors.lastname: Csajka
authors.forename: Thierry
authors.initials: T
authors.lastname: Buclin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000970:Antineoplastic Agents / Q000097:blood / Q000493:pharmacokinetics*; D001499:Bayes Theorem; D001549:Benzamides; D016903:Drug Monitoring / Q000379:methods*; D005260:Female; D046152:Gastrointestinal Stromal Tumors / Q000097:blood / Q000188:drug therapy*; D006801:Humans; D000068877:Imatinib Mesylate; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010879:Piperazines / Q000097:blood / Q000493:pharmacokinetics*; D047428:Protein Kinase Inhibitors / Q000097:blood / Q000493:pharmacokinetics*; D011743:Pyrimidines / Q000097:blood / Q000493:pharmacokinetics*; D015203:Reproducibility of Results; D055815:Young Adult
chemical_list: D000970:Antineoplastic Agents; D001549:Benzamides; D010879:Piperazines; D047428:Protein Kinase Inhibitors; D011743:Pyrimidines; D000068877:Imatinib Mesylate