Phxnlme: An R package that facilitates pharmacometric workflow of Phoenix NLME analyses. (March 2017)

• Record Type: Journal Article
• Authors: Lim, Chay Ngee; Liang, Shuang; Feng, Kevin; Chittenden, Jason; Henry, Ana; Mouksassi, Samer; Birnbaum, Angela K.
• Is Part Of: Computer methods and programs in biomedicine. Volume 140(2017)
• Page Start: 121
• Page End: 129
• Publication Date: 2017-03
• Subjects: Model diagnostics -- R package -- Pharmacometrics -- Phxnlme -- Phoenix NLME; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.12.002
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Pharmacometric analyses are integral components of the drug development process. The Phxnlme R package is a comprehensive tool that addresses current limitations of a popular pharmacometric software, Phoenix NLME. Major pharmacometric tasks such as nonlinear mixed effects modeling, basic model diagnostics, visual predictive checks and bootstrap can be implemented on R using Phxnlme . Phxnlme provides greater overall efficiency and improved customizable graphics compared to the desktop Phoenix NLME software, and is freely available for download on the CRAN repository. Abstract: Background and objective: Pharmacometric analyses are integral components of the drug development process, and Phoenix NLME is one of the popular software used to conduct such analyses. To address current limitations with model diagnostic graphics and efficiency of the workflow for this software, we developed an R package, Phxnlme, to facilitate its workflow and provide improved graphical diagnostics. Methods: Phxnlme was designed to provide functionality for the major tasks that are usually performed in pharmacometric analyses (i.e. nonlinear mixed effects modeling, basic model diagnostics, visual predictive checks and bootstrap). Various estimation methods for modeling using the R package are made available through the Phoenix NLME engine. The Phxnlme R package utilizes other packages such as ggplot2 and lattice to produce the graphical output, and various features were included to allow customizability of the output. Interactive features for some plots were also added using the manipulate R package. Results: Phxnlme provides enhanced capabilities for nonlinear mixed effects modeling that can be accessed using the phxnlme() command. Output from the model can be graphed to assess the adequacy of model fits and further explore relationships in the data using various functions included in this R package, such as phxplot() and phxvpc.plot(). Bootstraps, stratified up to three variables, can also be performed to obtain confidence intervals around the model estimates. With the use of an R interface, different R projects can be created to allow multi-tasking, which addresses the current limitation of the Phoenix NLME desktop software. In addition, there is a wide selection of diagnostic and exploratory plots in the Phxnlme package, with improvements in the customizability of plots, compared to Phoenix NLME. Conclusions: The Phxnlme package is a flexible tool that allows implementation of the analytical workflow of Phoenix NLME with R, with features for greater overall efficiency and improved customizable graphics. Phxnlme is freely available for download on the CRAN repository (https://cran.r-project.org/web/packages/Phxnlme/ ).