title: Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel.
abstract: OBJECTIVE
  Applying physiologically-based pharmacokinetic (PBPK) modelling in paediatric cancer drug development is still challenging. We aimed to demonstrate how PBPK modelling can be applied to optimize dose and sampling times for a paediatric pharmacokinetic (PK) bridging study in oncology and to compare with the allometric scaling population PK (AS-popPK) approach, using docetaxel as an example.
  
  
  METHODS
  A PBPK model for docetaxel was first developed for adult cancer patients using Simcyp® and subsequently used to predict its PK profiles in children by accounting for age-dependent physiological differences. Dose (mg m(-2) ) requirements for children aged 0-18 years were calculated to achieve targeted exposure in adults. Simulated data were then analyzed using population PK modelling with MONOLIX® in order to perform design optimization with the population Fisher information matrix (PFIM). In parallel, the AS-popPK approach was performed for the comparison.
  
  
  RESULTS
  The PBPK model developed for docetaxel adequately predicted its PK profiles in both adult and paediatric cancer patients (predicted clearance and volume of distribution within 1.5 fold of observed data). The revised dose of docetaxel for a child over 1.5 years old was higher than the adult dose. Considering clinical constraints, the optimal design contained two groups of 15 patients, having three or four sampling times and had good predicted relative standard errors (RSE<30%) for almost all parameters. The AS-popPK approach performed reasonably well but could not predict for very young children.
  
  
  CONCLUSIONS
  This research shows the clinical utility of PBPK modelling in combination with population PK modelling and optimal design to support paediatric oncology development.
authors.affiliation: Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, Paris, France.
authors.forename: Hoai-Thu
authors.initials: HT
authors.lastname: Thai
authors.affiliation: Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, Paris, France.
authors.forename: Florent
authors.initials: F
authors.lastname: Mazuir
authors.affiliation: Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, Paris, France.
authors.forename: Sylvaine
authors.initials: S
authors.lastname: Cartot-Cotton
authors.affiliation: Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, Paris, France.
authors.forename: Christine
authors.initials: C
authors.lastname: Veyrat-Follet
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000970:Antineoplastic Agents / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000627:therapeutic use; D002648:Child; D002675:Child, Preschool; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D000077143:Docetaxel; D055808:Drug Discovery; D006801:Humans; D007223:Infant; D008954:Models, Biological; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism*; D011237:Predictive Value of Tests; D043823:Taxoids / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000627:therapeutic use; D014018:Tissue Distribution
chemical_list: D000970:Antineoplastic Agents; D043823:Taxoids; D000077143:Docetaxel
keywords: docetaxel; optimal design; paediatrics; physiologically-based pharmacokinetics