Predictive performance of a semiparametric method to estimate population pharmacokinetic parameters using NONMEM.
Routine clinical pharmacokinetic (PK) data collected from patients receiving inulin were analyzed to estimate population PK parameters; 560 plasma concentration determinations for inulin were obtained from 90 patients. The data were analyzed using NONMEM. The population PK parameters were estimated using a Constrained Longitudinal Splines (CLS) semiparametric approach and a first-order conditional method (FOCE). The mean posterior individual clearance values were 7.73 L/hr using both parametric and semiparametric methods. This estimation was compared with clearances estimated using standard nonlinear weighted least squares approach (reference value, 7.64 L/hr). The bias was not statistically different from zero and the precision of the estimates was 0.415 L/hr using parametric method and 0.984 L/hr using semiparametric method. To evaluate the predictive performances of the population parameters, 17 new subjects were used. First, the individual inulin clearance values were estimated from drug concentration-time curve using a nonlinear weighted least-squares method then they were estimated using the NONMEM POSTHOC method obtained using parametric and CLS methods as well as an alternative method based on a Monte Carlo simulation approach. The population parameters combined with two individual inulin plasma concentrations (0.25 and 2 hr) led to an estimation of individual clearances without bias and with a good precision. This paper not only evaluates the relative performance of the parametric and the CLS methods for sparse data but also introduces a new method for individual estimation.
Bressolle
F
F
Laboratoire de pharmacocinétique, Faculté de Pharmacie, Université Montpellier I, France.
Gomeni
R
R
D003920:Diabetes Mellitus / Q000097:blood; D006801:Humans; D007444:Inulin / Q000493:pharmacokinetics*; D016018:Least-Squares Analysis; D008657:Metabolic Clearance Rate; D015233:Models, Statistical; D009765:Obesity; D011237:Predictive Value of Tests; D012984:Software
D007444:Inulin