title: Data supplementation: a pharmacokinetic/pharmacodynamic knowledge creation approach for characterizing an unexplored region of the response surface.
abstract: OBJECTIVE
  To develop a data supplementation [i.e., a pharmacokinetic/pharmacodynamics (PK/PD) knowledge creation] approach for generating supplemental data to be used in characterizing a targeted unexplored segment of the response surface.
  
  
  METHODS
  The procedure for data supplementation can be summarized as follows: 1) statement of the objective of data supplementation for PK/PD knowledge creation, 2) performance of PK knowledge discovery, 3) PK data synthesis for target dose group(s), 4) covariate data synthesis for virtual subjects in the target dose group(s), 5) discovery of hidden knowledge from real data set to which supplemental data will be added, 6) implementation of a data supplementation methodology, and 7) discovery and communication of the created knowledge. A nonparametric approximate Bayesian multiple supplementation and its modification, structure-based multiple supplementation, which is an adaptation of the approximate Bayesian bootstrap, is proposed as a method of data supplementation for PK/PD knowledge creation. The structured-based multiple supplementation methodology was applied to characterize the effect of a target dose of 100 mg that was unexplored in a previously concluded study that investigated the effect of 200- and 600-mg doses on biomarker response.
  
  
  RESULTS
  The target dose of 100 mg was found to produce a response comparable with that of the 200 mg and better than that obtained with the 600 mg.
  
  
  CONCLUSIONS
  Implementation of the PK/PD knowledge creation process through data supplementation resulted in gaining knowledge about a targeted region of a response surface (i.e., the effect of a target dose) that was not previously studied in a completed study without expending resources in conducting a new study.
authors.affiliation: Department of Clinical Pharmacology, Vertex Pharmaceuticals, Inc., Cambridge, Massachusetts 02139, USA. ene_ette@vrtx.com
authors.forename: Ene I
authors.initials: EI
authors.lastname: Ette
authors.forename: Hui-May
authors.initials: HM
authors.lastname: Chu
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Godfrey
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002626:Chemistry, Pharmaceutical; D003625:Data Collection / Q000379:methods*; D015195:Drug Design; D008954:Models, Biological; D010599:Pharmacokinetics; D012107:Research Design