A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R. Issue 127 (April 2016)

• Record Type: Journal Article
• Authors: Acharya, Chayan; Hooker, Andrew C.; Türkyılmaz, Gülbeyaz Yıldız; Jönsson, Siv; Karlsson, Mats O.
• Is Part Of: Computer methods and programs in biomedicine. Issue 127(2016)
• Page Start: 83
• Page End: 93
• Publication Date: 2016-04
• Subjects: Non-compartmental analysis (NCA) -- PK -- NONMEM -- Posterior predictive check -- Simulation-based diagnostic; 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.2016.01.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: ncappc performs (i) NCA and (ii) simulation-based posterior predictive checks using NCA metrics. ncappc package is highly flexible and comprehensive. Can perform both individual and population level diagnostics. Produces output summarizing the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. Abstract: Background and objective: Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration–time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks ( ppc ) for a population PK (PopPK) model using NCA metrics. Methods: The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration–time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. Results: The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration–time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. Conclusions: The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration–time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://cran.r-project.org/web/packages/ncappc/index.html/ ) and GitHub (https://github.com/cacha0227/ncappc/ ).