title: Comparative pharmacokinetic analysis by standard two-stage method versus nonparametric population modeling.
abstract: OBJECTIVE
  To compare the two-stage method, a widely used analytical method in pharmacokinetic studies, with nonparametric population modeling by using the same data set for determining the oral bioavailability of ribavirin.
  
  
  METHODS
  Pharmacokinetic analysis. Clinical research center.
  
  
  METHODS
  Oral bioavailability data of ribavirin determined previously in six healthy adults.
  
  
  METHODS
  After 13C3-ribavirin 150 mg intravenously and unlabeled ribavirin 400 mg orally had been given 1 hour apart, serial serum and urine samples were obtained for up to 169 hours. Concentrations of 13C3-ribavirin and unlabeled ribavirin in serum and urine were determined by a high-performance liquid chromatography tandem mass spectrometric method.
  
  
  RESULTS
  Serum and urine concentration-time profiles were comodeled with a three-compartment model. The analysis was performed again by using the nonparametric population analysis technique. Serum ribavirin concentrations underwent Monte Carlo simulation for 1000 subjects receiving a single 600-mg oral dose. Both methods were similar in determining the mean +/- SD bioavailability (51.8 +/- 21.8% by the two-stage method vs 54.8 +/- 16.4% by nonparametric modeling, p=0.79). However, the estimates of dispersion of model parameters and simulated drug exposures were substantially reduced by the population-modeling technique, as it takes into account covariance among model parameters and intersubject variability.
  
  
  CONCLUSIONS
  Although the study sample was small, our parallel analyses of the same data set clearly demonstrated that more precise parameter estimates are likely to result with the population-modeling technique. Having accurate and precise estimation of population pharmacokinetic parameters and their true variances is crucial, as, at any dose, there'will be a lower probability of encountering a concentration-driven toxicity because of fewer outliers as the variance associated with the parameters decreases.
authors.affiliation: Division of Clinical Pharmacology, Ordway Research Institute, Albany, NY 12208, USA.
authors.forename: Vincent H
authors.initials: VH
authors.lastname: Tam
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Preston
authors.forename: George L
authors.initials: GL
authors.lastname: Drusano
mesh_terms: D000328:Adult; D000998:Antiviral Agents / Q000097:blood / Q000493:pharmacokinetics* / Q000652:urine; D001682:Biological Availability; D002247:Carbon Isotopes; D002986:Clinical Trials as Topic; D003198:Computer Simulation / Q000706:statistics & numerical data; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012254:Ribavirin / Q000097:blood / Q000493:pharmacokinetics* / Q000652:urine; D018709:Statistics, Nonparametric
chemical_list: D000998:Antiviral Agents; D002247:Carbon Isotopes; D012254:Ribavirin