Comparison of NONMEM and Pmetrics Analysis for Aminoglycosides in Adult Patients with Cystic Fibrosis
Alghanem S(1), Neely M(2), Thomson A(1,3)
(1)Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK. (2)Laboratory of Applied Pharmacokinetics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. (3)Pharmacy Department, Western Infirmary, NHS Greater Glasgow and Clyde, Dumbarton Road, Glasgow G11 6NT, UK.
Objectives: The aim of this study was to compare the performance of parametric and nonparametric approaches in population pharmacokinetic analysis for cystic fibrosis patients.
Methods: The study involved a retrospective analysis of a database of aminoglycoside concentration measurements in patients with cystic fibrosis from Glasgow and The Hague. The data had previously been analysed using a traditional parametric population modelling approach using NONMEM (version 7) (1). The present analysis focuses on population analysis using a non-parametric approach with the software Pmetrics (2). One and two compartment models and the influence of covariates, using both simple and mechanistic approaches, were compared for parametric and non-parametric approaches.
Results: The combined dataset included 331 patients (166 from Glasgow) with 1490 courses of therapy and 3690 aminoglycoside concentration measurements. The NONMEM analysis found that a two compartment model was superior to a one compartment model. This was confirmed using the non-parametric approach where the two compartment model was again superior (-2 likelihood value 9004 vs 9618). The population estimates of clearance (CL) and V1 generated from NONMEM and Pmetrics were very similar. In addition, both NONMEM and Pmetrics identified creatinine clearance and height as factors influencing CL and height influencing V1.
Conclusions: Similar results were obtained when parametric and nonparametric approaches were used to analyse aminoglycoside data from patients with cystic fibrosis
 Beal S, Sheiner LB, Boeckman A et al. NONMEM User's Guides (1989-2009). Ellicott City, MD, USA: Icon Development Solutions, 2009.
 Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW. Accurate Detection of Outliers and Subpopulations with Pmetrics, a Nonparametric and Parametric Pharmacometric Modelling and Simulation Package for R. Ther Drug Monit 2012; 34:467-476.