The capability of Several Population-based Approach Software to Analyze Sparse Drug Plasma Concentration Data after Intra-Venous Bolus Injection

Akhmad Kharis Nugroho, Lukman Hakim


Monolix, NONMEM, and WinBUGS-PKBUGS are among available software package for population-based modeling. The sparse condition of drug plasma concentration versus time (Cp-time) data is prevalent in clinically based studies involving patients. It is not ethical in this case, to collect a many and large volumes of blood samples. This study was aimed to simulate the capability of Monolix, NONMEM, and WinBUGS-PKBUGS to analyze very sparse Cp-time data after an intravenous bolus drug administration and to estimate the minimum number of Cp-time data required for an adequate analysis. 

Data of Cp-time were obtained based on simulation using the pharmacokinetic one-compartment open model following an intravenous bolus administration of 50 mg of a hypothetical drug. In this respect, six random values of k (rate constant of elimination) and Vd (volume of distribution) with mean and standard deviation values of 0.3 ±0.1 per hour and 30 ± 10 L, respectively, were used to create simulated Cp-time data of 6 subjects. Simulated Cp-time data in each subject were randomly ranked to choose data based on the intended number of samples in each subject. Several sparse Cp-time data scenarios, starting from a very limited state, i.e., with a total of 6 Cp-time data (1 datum per subject) to a rich situation with 48 Cp data (8 data per subject), were examined.

The goodness of fit evaluations, as well as the similarity of individual values of k and Vd to the respective real values  (p>0.05), indicate that nonlinear-mixed-effect-model using Monolix, NONMEM and WinBUGS-PKBUGS can appropriately describe sparse Cp-time data even with only 2 data per subject. This fact is an important finding to support the demand of analytical tool for a limited number of Cp-time data such as obtained in therapeutic drug monitoring event.


Monolix, NONMEM, WinBUGS-PKBUGS, sparse data, therapeutic drug monitoring

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