title: Bayesian estimation of methotrexate pharmacokinetic parameters and area under the curve in children and young adults with localised osteosarcoma.
abstract: BACKGROUND
  Methotrexate is the most efficient anticancer drug in osteosarcoma. It requires individual exposure monitoring because of the high doses used, its wide interpatient pharmacokinetic variability and the existence of demonstrated relationships between efficacy, toxicity and serum drug concentrations.
  
  
  OBJECTIVE
  To develop a maximum a posteriori (MAP) Bayesian estimator able to predict individual pharmacokinetic parameters and exposure indices such as area under the curve (AUC) for methotrexate from a few blood samples, in order to prevent toxicity and facilitate further studies of the relationships between efficacy and exposure.
  
  
  METHODS
  Methotrexate population pharmacokinetics were estimated by a retrospective analysis of concentration data from 40 children and young adults by using the nonparametric expectation maximisation method NPEM. A linear two-compartment model with elimination from the central compartment was assumed. Individual pharmacokinetic parameters and AUC were subsequently estimated in 30 other young patients, using MAP Bayesian estimation as implemented in two programs, ADAPT II and an inhouse program Winphar((R)).
  
  
  RESULTS
  The pharmacokinetic parameters used in the model were the volume of the central compartment (V(1)) and the transfer constants (k(10), k(12) and k(21)). The mean values (with percentage coefficient of variation) obtained were: 18.24L (54.1%) and 0.41 (42.3%), 0.0168 (68.7%), and 0.1069 (61.3%) h(-1), respectively. Bayesian forecasting enabled nonbiased estimation of AUC and systemic clearance using a schedule with two sampling times (6 and 24 hours after the beginning of the infusion) and either program. Collection of a third sample at 4 hours improved the precision.
  
  
  CONCLUSIONS
  The Bayesian adaptive method developed herein allows accurate estimation of individual exposure to methotrexate and can easily be used in clinical practice.
authors.affiliation: Department of Pharmacology and Toxicology, University Hospital, Limoges, France. rousseau@pharm.unilim.fr
authors.forename: Annick
authors.initials: A
authors.lastname: Rousseau
authors.forename: Christophe
authors.initials: C
authors.lastname: Sabot
authors.forename: Nicole
authors.initials: N
authors.lastname: Delepine
authors.forename: Gerard
authors.initials: G
authors.lastname: Delepine
authors.forename: Jean
authors.initials: J
authors.lastname: Debord
authors.forename: Gerard
authors.initials: G
authors.lastname: Lachâtre
authors.forename: Pierre
authors.initials: P
authors.lastname: Marquet
mesh_terms: D000293:Adolescent; D000328:Adult; D000964:Antimetabolites, Antineoplastic / Q000097:blood / Q000493:pharmacokinetics* / Q000627:therapeutic use; D019540:Area Under Curve; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016014:Linear Models; D008727:Methotrexate / Q000097:blood / Q000493:pharmacokinetics* / Q000627:therapeutic use; D008954:Models, Biological; D012516:Osteosarcoma / Q000188:drug therapy / Q000378:metabolism*; D012189:Retrospective Studies; D012984:Software
chemical_list: D000964:Antimetabolites, Antineoplastic; D008727:Methotrexate