Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-menten model: Routine clinical pharmacokinetic data

Abstract

Individual pharmacokinetic par parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual intraindividual variability plus measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters are estimated either by fitting all individual's data together as though there was no individual kinetic differences (the naive pooled data approach), or by fitting each individual's data separately, and then combining the individual parameter estimates (the two-stage approach). A third approach, NONMEM, takes a middle course between these, and avoids shortcomings of each of them. A data set consisting of 124 steady-state phenytoin concentration-dosage pairs from 49 patients, obtained in the routine course of their therapy, was analyzed by each method. The resulting population parameter estimates differ considerably (population mean Km, for example, is estimated as 1.57, 5.36, and 4.44 micrograms/ml by the naive pooled data, two-stage, and NONMEN approaches, respectively). Simulations of the data were analyzed to investigate these differences. The simulations indicate that the pooled data approach fails to estimate variabilities and produces imprecise estimates of mean kinetics. The two-stage approach produces good estimates of mean kinetics, but biased and imprecise estimates of interindividual variability. NONMEN produces accurate and precise estimates of all parameters, and also reasonable confidence intervals for them. This performance is exactly what is expected from theoretical considerations and provides empirical support for the use of NONMEM when estimating population pharmacokinetics from routine type patient data.

Prolonged Acetaminophen-Protein Adduct Elimination During Renal Failure, Lack of Adduct Removal by Hemodiafiltration, and Urinary Adduct Concentrations After Acetaminophen Overdose

“…Elimination was fitted using a singlecompartment model using Simulation Analysis and Modeling Software (SAAM II) and PopKinetics population analysis software, versions 2.3.1 (The Epsilon Group, Charlottesville, VA). Population analysis approaches are considered to provide a smaller bias in estimation of a group's pharmacokinetic parameters [13]. With ten subjects in each group and a twotailed alpha of 0.05, we calculated power >0.95 to detect a 30 % difference in elimination half-life between groups.…”

Mixed effect models for predicting breast height diameter from stump diameter of Oriental beech in Göldağ

“…Yang and Huang (2011) compared linearization through a firstorder Taylor series expansion around random effects using the first-order (FO) approximation of Sheiner and Beal (1980) and the first-order conditional expectation (FOCE) approximation of Lindstrom and Bates (1990) to estimate dominant height-age relationships. These prediction techniques, FO and FOCE, for the parameters of the model may be a better choice especially, since it may be that more advanced and complicated modeling technique accounting for DBH variations may be required for effective and more predictive DBH estimations.…”

Ethanol kinetics: extent of error in back extrapolation procedures [see comments]

“…The identification of circumstances in which genetic, physiological, environmental, pathological and other factors play a significant role in ethanol kinetics may be useful by allowing the minimisation of individual extrapolation errors. This approach has already been found to be useful in the area of therapeutic drug monitoring and the design of dosing regimens (Sheiner et al, 1980;Whiting et al, 1986). Lack of cooperation from suspected offenders may however compromise such an approach.…”