title: Population pharmacokinetics and Bayesian estimation of cyclosporine in a Tunisian population of hematopoietic stem cell transplant recipient.
abstract: OBJECTIVE
  Therapeutic drug monitoring of cyclosporine minimizes the risk of toxicity and acute rejection after transplantation. Areas under the curve (AUCs) rather than trough concentration-based monitoring are recommended. Population pharmacokinetics (PopPK) modeling and Bayesian estimation seem to be the best way to predict cyclosporine disposition and dose requirements to achieve the therapeutic target in an individual patient because of the possibility of predicting cyclosporine AUC using only a few blood samples. Our objectives were to build a PopPk model for cyclosporine in a Tunisian population of HSCT patients and to develop a Bayesian method for the estimation of individual cyclosporine AUC.
  
  
  METHODS
  The PopPk of cyclosporine was studied using nonlinear mixed effects modeling (NONMEM) in 30 patients (index group) receiving cyclosporine on a twice-daily basis. Ten blood samples were collected after steady-state morning cyclosporine dose. Bayesian estimation of individual AUC was made on the basis of three blood concentration measurements in an independent group of 30 patients (test group).
  
  
  RESULTS
  A two-compartment model with first-order absorption and a lag time provided the best fitting. The population mean estimate and interindividual variability from the final model for CL, Ka, Tlag, V1, V2, and Q were 25.4 L/h (CV = 38.72 %), 0.214 h(-1)(CV = 28.5 %), 0.382 h, 10.9 L (85.73 %), 496 L, and 5 L/h, respectively. Covariates had no discernible effects on cyclosporine pharmacokinetics in our population. Bayesian estimation provided an accurate estimation of AUC, although a bias was observed leading to slight underprediction of AUC (bias -1.03 %). A very satisfactory precision was observed (RMSE 12.07 %).
  
  
  CONCLUSIONS
  We report a PopPK model for cyclosporine in Tunisian HSCT patients. Bayesian estimation using only three concentrations provides good prediction of cyclosporine exposure. These tools allow us to routinely estimate cyclosporine AUC in a clinical setting.
authors.affiliation: Laboratoire de Pharmacologie Clinique, Centre National de Pharmacovigilance, 9 Avenue Dr. Zouheïr Essafi, 1006, Tunis, Tunisia. eljebarihanene@yahoo.fr
authors.forename: Hanene
authors.initials: H
authors.lastname: Eljebari
authors.forename: Emna
authors.initials: E
authors.lastname: Gaies
authors.forename: Nadia Ben
authors.initials: NB
authors.lastname: Fradj
authors.forename: Nadia
authors.initials: N
authors.lastname: Jebabli
authors.forename: Issam
authors.initials: I
authors.lastname: Salouage
authors.forename: Sameh
authors.initials: S
authors.lastname: Trabelsi
authors.forename: Mohamed
authors.initials: M
authors.lastname: Lakhal
authors.forename: Anis
authors.initials: A
authors.lastname: Klouz
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D002648:Child; D016572:Cyclosporine / Q000009:adverse effects / Q000378:metabolism / Q000493:pharmacokinetics* / Q000627:therapeutic use; D016903:Drug Monitoring / Q000379:methods; D005260:Female; D006086:Graft vs Host Disease / Q000517:prevention & control; D006207:Half-Life; D018380:Hematopoietic Stem Cell Transplantation / Q000009:adverse effects*; D006801:Humans; D007166:Immunosuppressive Agents / Q000009:adverse effects / Q000378:metabolism / Q000493:pharmacokinetics* / Q000627:therapeutic use; D007408:Intestinal Absorption; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D014416:Tunisia; D055815:Young Adult
chemical_list: D007166:Immunosuppressive Agents; D016572:Cyclosporine