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Journal of Clinical Pharmacology 1996-Apr

Nonparametric expectation maximization population modeling of ganciclovir.

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S L Preston
G L Drusano

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Abstracto

The use of the nonparametric expectation maximization (NPEM2) program to estimate pharmacokinetic parameters of ganciclovir in a group of patients with human immunodeficiency virus (HIV) and cytomegalovirus (CMV) infection was evaluated. A 10-point data set per patient obtained over 8 hours was analyzed. Mean pharmacokinetic parameters obtained included rate constant from the central to the peripheral compartment (KCP,3.1 hr-1), rate constant from the peripheral to the central compartment (KPC, 0.824 hr-1), slope of the volume of distribution to body weight (VS, 0.246 L/kg), and slope of clearance to creatinine clearance (Cl(cr)) and body weight (CLS,0.222L/hr/kg/100 mL/ min Cl(cr). Use of NPEM2 led to identification of a subset of patients with CMV retinitis who had a more rapid clearance of ganciclovir of 0.51 to 0.54 L/hr/kg/100 mL/min Cl(cr). Use of smaller, optimally timed samples of five, four, and three data points per patient produced mean pharmacokinetic parameter results consistent with the full ten-point data set. When Bayesian-derived parameter estimates using a five-point data set were compared with a traditional, nonlinear, least-square analysis of the entire ten-point data set, estimates of clearance were determined to be relatively unbiased and precise. The ability of NPEM2 to estimate pharmacokinetic parameters and to determine the population distribution of the parameters was demonstrated. By using points in the analysis chosen by D-optimal design theory, NPEM2 was able to give consistent parameter estimates with as few as three data points. Determination of the distribution appeared to have been dependent on the time points used, however. The approach of MAP-Bayesian analysis to derive patient-specific estimates using optimal samples and prior estimates from a previous population pharmacokinetic analysis for inclusion in subsequent pharmacodynamic analyses of drug exposure (area under the concentration-time curve) may enable development of exposure-response and exposure-toxicity relationships.

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