title: Bayesian approach for the estimation of cyclosporine area under the curve using limited sampling strategies in pediatric hematopoietic stem cell transplantation.
abstract: BACKGROUND
  The optimal marker for cyclosporine (CsA) monitoring in transplantation patients remains controversial. However, there is a growing interest in the use of the area under the concentration-time curve (AUC), particularly for cyclosporine dose adjustment in pediatric hematopoietic stem cell transplantation. In this paper, we develop Bayesian limited sampling strategies (B-LSS) for cyclosporine AUC estimation using population pharmacokinetic (Pop-PK) models and investigate related issues, with the aim to improve B-LSS prediction performance.
  
  
  METHODS
  Twenty five pediatric hematopoietic stem cell transplantation patients receiving intravenous and oral cyclosporine were investigated. Pop-PK analyses were carried out and the predictive performance of B-LSS was evaluated using the final Pop-PK model and several related ones. The performance of B-LSS when targeting different versions of AUC was also discussed.
  
  
  RESULTS
  A two-compartment structure model with a lag time and a combined additive and proportional error is retained. The final covariate model does not improve the B-LSS prediction performance. The best performing models for intravenous and oral cyclosporine are the structure ones with combined and additive error, respectively. Twelve B-LSS, consisting of 4 or less sampling points obtained within 4 hours post-dose, predict AUC with 95th percentile of the absolute values of relative prediction errors of 20% or less. Moreover, B-LSS perform better for the prediction of the 'underlying' AUC derived from the Pop-PK model estimated concentrations that exclude the residual errors, in comparison to their prediction of the observed AUC directly calculated using measured concentrations.
  
  
  CONCLUSIONS
  B-LSS can adequately estimate cyclosporine AUC. However, B-LSS performance is not perfectly in line with the standard Pop-PK model selection criteria; hence the final model might not be ideal for AUC prediction purpose. Therefore, for B-LSS application, Pop-PK model diagnostic criteria should additionally account for AUC prediction errors.
authors.forename: Sarem
authors.initials: S
authors.lastname: Sarem
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.forename: Olivier
authors.initials: O
authors.lastname: Barriere
authors.forename: Catherine
authors.initials: C
authors.lastname: Litalien
authors.forename: Yves
authors.initials: Y
authors.lastname: Théorêt
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Lapeyraque
authors.affiliation: Faculty of Pharmacy, Université de Montréal, C,P, 6128, Succ, Centre-ville, H3C 3J7 Montreal, Canada. fahima.nekka@umontreal.ca.
authors.forename: Fahima
authors.initials: F
authors.lastname: Nekka
mesh_terms: D000293:Adolescent; D019540:Area Under Curve; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D016572:Cyclosporine / Q000493:pharmacokinetics*; D005260:Female; D018380:Hematopoietic Stem Cell Transplantation; D006801:Humans; D007166:Immunosuppressive Agents / Q000493:pharmacokinetics*; D007223:Infant; D008297:Male
chemical_list: D007166:Immunosuppressive Agents; D016572:Cyclosporine