Predictive performance of a semiparametric method to estimate population pharmacokinetic parameters using NONMEM

Routine clinical pharmacokinetic (PK) data collected from patients receivin g inulin were analyzed to estimate population PK parameters; 560 plasma con centration determinations for inulin were obtained from 90 patients. The da ta 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 clea rance values were 7.73 L/hr using both parametric and semiparametric method s. This estimation was compared with clearances estimated using standard no nlinear weighted least squares approach (reference value, 7.64 L/hr). The b ias was not statistically different from zero and the precision of the esti mates was 0.415 L/hr using parametric method and 0.984 L/hr using semiparam etric method. To evaluate the predictive performances of the population par ameters, 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 POS THOC method obtained using parametric and CLS methods as well as an alterna tive method based on a Monte Carlo simulation approach. The population para meters 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 nor only evaluates the relative performance of the parametric and the CLS methods for sparse data but also introduces a ne w method for individual estimation.