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Abstract
Although it has been developed since 1972, the implementation of a population-based modeling approach in Indonesia, particularly to analyze biopharmaceutics and pharmacokinetics data is still very limited. This study was aimed to evaluate the performance of Monolix and NONMEM, two of the popular software packages in a population-based modeling approach, to analyze the limited data (sparse sampling data) of the time profiles of the simulated plasma drug concentration of a theoretical compound. and NONMEM were used to model the limited data (40 data points) as a results of the random selection from the 180 point data of simulated plasma drug concentration (Cp) on 20 subjects at 0.25; 0.5; 0.75; 1; 1.5; 3; 6; 12 and 18 hours after per-oral administration of a 100mg of a theoretical compound. Population values of the absorption rate constant (Ka), the elimination rate constant (Kel) and volume of distribution (Vd) were compared to the average Ka, Kel and Vd obtained by the conventional method (two stage approach) using PKSolver on the Cp data of all subjects. The calculation system of a nonlinear mixed effect model in Monolix and NONMEM, successfully describes the sparse data, based on the visual evaluation of the goodness of fit. Comparison of parameter estimates of population values in Monolix and NONMEM are in the range of 94 to 108% of the real values of the rich data analysed by PKSolver. A population-based modeling can adequately analyze limited or sparse data, demonstrating its capability as an important tool in clinical studies, involving patients.
Highlights
Population-based modeling approach has been developed since 1972, by introduction and development of the so called nonlinear mixed effect model (Sheiner et al, 1972)
NONMEM was introduced by Lewis Sheiner, Stuart Beal and NONMEM Project Group at University of California as the first software package capable to handle such
Monolix analyses the sparse data in several type of calculation
Summary
Population-based modeling approach has been developed since 1972, by introduction and development of the so called nonlinear mixed effect model (Sheiner et al, 1972) In this method, a certain parameter or variable, for example the rate constant of absorption, Ka, is considered determined by a population or fixed effect value and an interindividual variability, resulted in a different Ka parameter value in each subject. As such approach directly focuses on the population data, it allows analyses based on the sparse sampling data, which is commonly obtained in clinical studies involving patients (Sheiner et al, 1972). The nonlinear mixed effect model is able to correlate a certain parameter such as Ka, clearance (CL), distribution volume (Vd) or elimination rate constant (Kel) to certain covariates such as sex, age, body weight in a quantitative manner, allowing a better description and correlation of the population data to those covariates (Jonsson and Karlsson, 1998; Wählby et al, 2001).
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