title: Warfarin dose prediction in children using pharmacometric bridging--comparison with published pharmacogenetic dosing algorithms.
abstract: OBJECTIVE
  Numerous studies have investigated causes of warfarin dose variability in adults, whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.
  
  
  METHODS
  An adult population pharmacokinetic/pharmacodynamic (PK/PD) model for warfarin, with CYP2C9 and VKORC1 genotype, age and target international normalized ratio (INR) as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external data set of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children.
  
  
  RESULTS
  Overall, the bridged model predicted INR response well in 64 warfarin-treated Swedish children (median age 4.3 years), but with a tendency to overpredict INR in children ≤2 years old. The bridged model predicted 20 of 49 children (41 %) within ± 20 % of actual maintenance dose (median age 7.2 years). In comparison, the published dosing algorithms predicted 33-41 % of the children within ±20 % of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ±20 % of actual dose to 70 %.
  
  
  CONCLUSIONS
  A mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.
authors.affiliation: Department of Medical Sciences, Clinical Pharmacology, Uppsala University, Entrance 61 3rd floor, SE-751 85, Uppsala, Sweden. anna-karin.hamberg@medsci.uu.se
authors.forename: Anna-Karin
authors.initials: AK
authors.lastname: Hamberg
authors.forename: Lena E
authors.initials: LE
authors.lastname: Friberg
authors.forename: Katarina
authors.initials: K
authors.lastname: Hanséus
authors.forename: Britt-Marie
authors.initials: BM
authors.lastname: Ekman-Joelsson
authors.forename: Jan
authors.initials: J
authors.lastname: Sunnegårdh
authors.forename: Anders
authors.initials: A
authors.lastname: Jonzon
authors.forename: Bo
authors.initials: B
authors.lastname: Lundell
authors.forename: E Niclas
authors.initials: EN
authors.lastname: Jonsson
authors.forename: Mia
authors.initials: M
authors.lastname: Wadelius
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage* / Q000493:pharmacokinetics*; D001189:Aryl Hydrocarbon Hydroxylases / Q000235:genetics / Q000378:metabolism; D001777:Blood Coagulation / Q000187:drug effects*; D002648:Child; D002675:Child, Preschool; D065729:Cytochrome P-450 CYP2C9; D054796:Drug Dosage Calculations; D016903:Drug Monitoring / Q000379:methods; D005260:Female; D005838:Genotype; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D019934:International Normalized Ratio; D008297:Male; D006899:Mixed Function Oxygenases / Q000235:genetics / Q000378:metabolism; D008954:Models, Biological; D010597:Pharmacogenetics; D010641:Phenotype; D013548:Sweden; D064417:Vitamin K Epoxide Reductases; D014859:Warfarin / Q000008:administration & dosage* / Q000493:pharmacokinetics*
chemical_list: D000925:Anticoagulants; D014859:Warfarin; D006899:Mixed Function Oxygenases; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; D001189:Aryl Hydrocarbon Hydroxylases; C578523:VKORC1 protein, human; D064417:Vitamin K Epoxide Reductases