The capability of Several Population-based Approach Software to Analyze Sparse Drug Plasma Concentration Data after Intra-Venous Bolus Injection
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.
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. https://doi.org/10.1007/s10928-010-9175-z
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.
Keizer, R.J., Karlsson, M.O., Hooker, A., 2013. Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose. CPT Pharmacomet. Syst. Pharmacol. 2, e50. https://doi.org/10.1038/psp.2013.24
Lavielle, M., 2014. Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools. CRC Press.
Lavielle, M., Mentré, F., 2007. Estimation of Population Pharmacokinetic Parameters of Saquinavir in HIV Patients with the MONOLIX Software. J. Pharmacokinet. Pharmacodyn. 34, 229–249. https://doi.org/10.1007/s10928-006-9043-z
Liengme, B., 2015. A Guide to Microsoft Excel 2013 for Scientists and Engineers. Academic Press.
Lunn, D.J., Best, N., Thomas, A., Wakefield, J., Spiegelhalter, D., 2002. Bayesian analysis of population PK/PD models: general concepts and software. J. Pharmacokinet. Pharmacodyn. 29, 271–307.
Miller, W., 2012. OpenStat Reference Manual. Springer Science & Business Media.
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. https://doi.org/10.1093/bja/aes025
Ntzoufras, I., 2011. Bayesian Modeling Using WinBUGS. John Wiley & Sons.
Nugroho, A.K., Hakim, A.R., Hakim, L., 2017. Population-Based Approach to Analyze Sparse Sampling Data in Biopharmaceutics and Pharmacokinetics using Monolix and NONMEM. Indones. J. Pharm. 28, 205. https://doi.org/10.14499/indonesianjpharm28iss4pp205
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. https://doi.org/10.1097/00007691-199806000-00008
Plan, E.L., Maloney, A., Mentré, F., Karlsson, M.O., Bertrand, J., 2012. Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models. AAPS J. 14, 420–432. https://doi.org/10.1208/s12248-012-9349-2
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. https://doi.org/10.12691/ajps-1-2-1
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.
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. https://doi.org/10.1128/AAC.02904-14
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