Population-Based Approach to Analyze Sparse Sampling Data in Biopharmaceutics and Pharmacokinetics using Monolix and NONMEM
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.
Chan, P.L.S., Jacqmin, P., Lavielle, M., McFadyen, L., Weatherley, B., 2011. The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects. J. Pharmacokinet. Pharmacodyn. 38:41–61.
Dartois, C., Lemenuel-Diot, A., Laveille, C., Tranchand, B., Tod, M., Girard, P., 2007. Evaluation of uncertainty parameters estimated by different population PK software and methods. J. Pharmacokinet. Pharmacodyn. 34:289–311.
Frame, B., 2006. Mixture Modeling with NONMEM V, in: Ette, E.I., Williams, P.J. (Eds.), Pharmacometrics: The Science of Quantitative Pharmacology. John Wiley & Sons, Inc, Hoboken, New Jersey, pp. 723–757.
Jonsson, E.N., Karlsson, M.O., 1998. Automated covariate model building within NONMEM. Pharm. Res. 15:1463–1468.
Liengme, B., 2015. A Guide to Microsoft Excel 2013 for Scientists and Engineers. Academic Press.
Mohammed, B.S., Engelhardt, T., Cameron, G.A., Cameron, L., Hawksworth, G.M., Hawwa, A.F., McElnay, J., Helms, P.J., McLay, J.S., 2012. Population pharmacokinetics of single-dose intravenous paracetamol in children. Br. J. Anaesth. 108:823–829.
Owen, J.S., Fiedler-Kelly, J., 2014. Introduction to population pharmacokinetic/pharmacodynamic analysis with nonlinear mixed effects models. Wiley, Hoboken, New Jersey.
Parke, J., Charles, B.G., 1998. NONMEM Population Pharmacokinetic Modeling of Orally Administered Cyclosporine From Routine Drug Monitoring Data After Heart Transplantation: Ther. Drug Monit. 20:284–293.
Sandström, M., Karlsson, M.O., Ljungman, P., Hassan, Z., Jonsson, E.N., Nilsson, C., Ringden, O., Oberg, G., Bekassy, A., Hassan, M., 2001. Population pharmacokinetic analysis resulting in a tool for dose individualization of busulphan in bone marrow transplantation recipients. Bone Marrow Transplant. 28:657–664.
Shaker, E., Hamadi, S., Idkaidek, N., E Blakey, G., Al-Saleh, A., 2013. Therapeutic Drug Monitoring and Population Pharmacokinetics of Digoxin in Jordanian Patients. Am. J. Pharmacol. Sci. 1:15–21.
Sheiner, L.B., Rosenberg, B., Melmon, K.L., 1972. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput. Biomed. Res. 5:411–459.
Wählby, U., Jonsson, E.N., Karlsson, M.O., 2001. Assessment of actual significance levels for covariate effects in NONMEM. J. Pharmacokinet. Pharmacodyn. 28:231–252.
Zhang, Y., Huo, M., Zhou, J., Xie, S., 2010. PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel. Comput. Methods Programs Biomed. 99:306–314.
Zheng, S., Matzneller, P., Zeitlinger, M., Schmidt, S., 2014. Development of a Population Pharmacokinetic Model Characterizing the Tissue Distribution of Azithromycin in Healthy Subjects. Antimicrob. Agents Chemother. 58:6675–6684.
- There are currently no refbacks.
Copyright (c) 2017 INDONESIAN JOURNAL OF PHARMACY
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Indonesian J Pharm indexed by:
View My Stats