Population-Based Approach to Analyze Sparse Sampling Data in Biopharmaceutics and Pharmacokinetics using Monolix and NONMEM

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.

Keywords

• model
• population
• sparse sampling data
• Monolix
• NONMEM