The PKNCA R package is designed to perform all noncompartmental analysis (NCA) calculations for pharmacokinetic (PK) data. The package is broadly separated into two parts (calculation and summary) with some additional housekeeping functions.
The primary and secondary goals of the PKNCA package are to 1) only give correct answers to the specific questions being asked and 2) automate as much as possible to simplify the task of the analyst. When automation would leave ambiguity or make a choice that the analyst may have an alternate preference for, it is either not used, is possible to override or raises an error message.
Note that backward compatibility will not be guaranteed until version 1.0. Argument and function changes will continue until then. These will be especially noticeable around the inclusion of IV NCA parameters and additional specifications of the drug, which include dose amount and route of administration.
Citation information for the PKNCA package is available with a call to
citation(package="PKNCA"). The preferred citation until publication
of version 1.0 is below:
Denney W, Duvvuri S and Buckeridge C (2015). "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package." Journal of Pharmacokinetics and Pharmacodynamics, 42(1), pp. 11-107,S65. ISSN 1573-8744, doi: 10.1007/s10928-015-9432-2, <URL: https://github.com/billdenney/pknca>.
The current stable version of PKNCA is available on CRAN. You can install it and its dependencies using the following command:
install.packages("PKNCA")
To install the development version from GitHub, type the following commands:
install.packages("remotes")
remotes::install_github("billdenney/pknca")
# Load the package
library(PKNCA)
# Set the business rule options with the PKNCA.options() function
# Load your concentration-time data
conc_raw <- read.csv("myconc.csv", stringsAsFactors=FALSE)
# Load your dose data
dose_raw <- read.csv("mydose.csv", stringsAsFactors=FALSE)
# Put your concentration data into a PKNCAconc object
o_conc <- PKNCAconc(data=conc_raw,
formula=conc~time|treatment+subject/analyte)
# Put your dose data into a PKNCAdose object
o_dose <- PKNCAdose(data=dose_raw,
formula=dose~time|treatment+subject)
# Combine the two (and automatically determine the intervals of
# interest
o_data <- PKNCAdata(o_conc, o_dose)
# Compute the NCA parameters
o_results <- pk.nca(o_data)
# Summarize the results
summary(o_results)
More help is available in the function help files. Be sure to look at the PKNCA.options function for choices on making PKNCA conform to your company’s business rules on calculation and summarization.
Please use the github issues page (https://github.com/billdenney/pknca/issues) to make feature requests and bug reports.