title: Advantages of prostate-specific antigen (PSA) clearance model over simple PSA half-life computation to describe PSA decrease after prostate adenomectomy.
abstract: OBJECTIVE
  A population kinetic approach based on PSA clearance (CL(PSA)) may be a more rational strategy to characterize prostate-specific antigen (PSA) decrease profile after prostate surgery than the commonly used method (half-life from mono/bi-exponential models).
  
  
  METHODS
  We used 182 post-adenomectomy PSA concentrations from 56 benign prostatic hyperplasia patients to build, with NONMEM software, a multi-exponential and a CL(PSA) model for comparison.
  
  
  RESULTS
  The best multi-exponential model was PSA(t)=4.96e(-)(0.269t)+3.10e(-)(0.16t)+0.746e(+)(0.0002t) with a stable median residual PSA at 0.64 ng/mL. The best model parametrized with clearance was CL(PSA)=0.0229()(AGE/69)(3.78). Akaike information criteria and standard errors favored the CL(PSA) model. Median peripheral zone and transitional zone productions were 0.034 ng/mL/cm(3) and 0.136 ng/mL/g. A threshold at 2 ng/mL on day 90 allowed for a diagnostic of biochemical relapse diagnostic.
  
  
  CONCLUSIONS
  The population CL(PSA) model was superior to the multi-exponential approach for investigating individual post-adenomectomy PSA decreases.
authors.affiliation: Université de Lyon, Lyon, F-69003, France. benoit.you@chu-lyon.fr
authors.forename: Benoit
authors.initials: B
authors.lastname: You
authors.forename: Paul
authors.initials: P
authors.lastname: Perrin
authors.forename: Gilles
authors.initials: G
authors.lastname: Freyer
authors.forename: Alain
authors.initials: A
authors.lastname: Ruffion
authors.forename: Brigitte
authors.initials: B
authors.lastname: Tranchand
authors.forename: Emilie
authors.initials: E
authors.lastname: Hénin
authors.forename: Philippe
authors.initials: P
authors.lastname: Paparel
authors.forename: Benjamin
authors.initials: B
authors.lastname: Ribba
authors.forename: Marian
authors.initials: M
authors.lastname: Devonec
authors.forename: Claire
authors.initials: C
authors.lastname: Falandry
authors.forename: Cécile
authors.initials: C
authors.lastname: Fournel
authors.forename: Michel
authors.initials: M
authors.lastname: Tod
authors.forename: Pascal
authors.initials: P
authors.lastname: Girard
mesh_terms: D000236:Adenoma / Q000097:blood / Q000601:surgery*; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood / Q000378:metabolism; D001772:Blood Cell Count; D001798:Blood Proteins / Q000378:metabolism; D003404:Creatinine / Q000097:blood; D006801:Humans; D007700:Kinetics; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D017430:Prostate-Specific Antigen / Q000097:blood / Q000378:metabolism*; D011470:Prostatic Hyperplasia / Q000097:blood / Q000601:surgery; D011471:Prostatic Neoplasms / Q000097:blood / Q000601:surgery*
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D003404:Creatinine; D017430:Prostate-Specific Antigen