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The PKNCA R Package

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 or is possible to override.

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 dosing including dose amount and route.

Citation

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>.

Installation

From CRAN

The current stable version of PKNCA is available on CRAN. You can install it and its dependencies using the following command:

install.packages("PKNCA")

From GitHub

To install the development version from GitHub, install the devtools package and then type the following commands:

install.packages("devtools")
install.packages("Rcpp")
library(devtools)
install_github("billdenney/pknca")

Calculating parameters

# Load the package
library(PKNCA)
# Set the business rule options with the PKNCA.options() function
# Load your concentration-time data
myrawconcdata <- read.csv("myconc.csv", stringsAsFactors=FALSE)
# Load your dose data
myrawdosedata <- read.csv("mydose.csv", stringsAsFactors=FALSE)
# Put your concentration data into a PKNCAconc object
myconc <- PKNCAconc(data=myrawconcdata,
                    formula=conc~time|treatment+subject/analyte)
# Put your dose data into a PKNCAdose object
mydose <- PKNCAdose(data=myrawdosedata,
                    formula=dose~time|treatment+subject)
# Combine the two (and automatically determine the intervals of
# interest
mydata <- PKNCAdata(myconc, mydose)
# Compute the NCA parameters
myresults <- pk.nca(mydata)
# Summarize the results
summary(myresults)

More help is available in the function help files, and be sure to look at the PKNCA.options function for many choices to make PKNCA conform to your company's business rules for calculations and summarization.

Feature requests

Please use the github issues page (https://github.com/billdenney/pknca/issues) to make feature requests and bug reports.

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Version

Install

install.packages('PKNCA')

Monthly Downloads

2,425

Version

0.9.4

License

AGPL-3

Issues

40

Pull Requests

10

Stars

77

Forks

27

Maintainer

Bill Denney

Last Published

June 1st, 2020

Functions in PKNCA (0.9.4)

PKNCAdose

Create a PKNCAdose object
PKNCA.options

Set default options for PKNCA functions
PKNCA.options.describe

Describe a PKNCA.options option by name.
AIC.list

Assess the AIC for all models in a list of models
PKNCAconc

Create a PKNCAconc object
PKNCAresults

Generate a PKNCAresults object
PKNCA.choose.option

Choose either the value from an option list or the current set value for an option.
PKNCAdata

Create a PKNCAdata object.
PKNCA.set.summary

Define how NCA parameters are summarized.
add.interval.col

Add columns for calculations within PKNCA intervals
addProvenance

Add a hash and associated information to enable checking object provenance.
get.best.model

Extract the best model from a list of models using AIC.list.
business.mean

Generate functions to do the named function (e.g. mean) applying the business rules.
geomean

Compute the geometric mean, sd, and CV
parseFormula

Parse a formula into its component parts.
formula.PKNCAconc

Extract the formula from a PKNCAconc object.
checkProvenance

Check the hash of an object to confirm its provenance.
fit_half_life

Perform the half-life fit given the data. The function simply fits the data without any validation. No selection of points or any other components are done.
findOperator

Find the first occurrence of an operator in a formula and return the left, right, or both sides of the operator.
check.interval.specification

Check the formatting of a calculation interval specification data frame.
check.conc.time

Verify that the concentration and time are valid
check.conversion

Check that the conversion to a data type does not change the number of NA values
pk.calc.clr

Calculate renal clearance
pk.calc.dn

Determine dose normalized NCA parameter
getAttributeColumn

Retrieve the value of an attribute column.
merge.splitlist

Merge two or more lists with a data.frame 'groupid' attribute defining the matching.
check.interval.deps

Take in a single row of an interval specification and return that row updated with any additional calculations that must be done to fulfill all dependencies.
formula.parseFormula

Convert the parsed formula back into the original
getData.PKNCAconc

Extract all the original data from a PKNCAconc or PKNCAdose object
getData.PKNCAdata

Extract all the original data from a PKNCAconc or PKNCAdose object
pk.calc.c0

Estimate the concentration at dosing time for an IV bolus dose.
pk.calc.cav

Calculate the average concentration during an interval.
interp.extrap.conc

Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.
pk.calc.clast.obs

Determine the last observed concentration above the limit of quantification (LOQ).
pk.calc.deg.fluc

Determine the degree of fluctuation
get.parameter.deps

Get all columns that depend on a parameter
clean.conc.blq

Handle BLQ values in the concentration measurements as requested by the user.
pk.calc.vz

Calculate the terminal volume of distribution (Vz)
getColumnValueOrNot

Get the value from a column in a data frame if the value is a column there, otherwise, the value should be a scalar or the length of the data.
choose.auc.intervals

Choose intervals to compute AUCs from time and dosing information
pk.business

Run any function with a maximum missing fraction of X and 0s possibly counting as missing. The maximum fraction missing comes from PKNCA.options("max.missing").
getData.PKNCAresults

Extract all the original data from a PKNCAconc or PKNCAdose object
pk.nca

Compute NCA parameters for each interval for each subject.
pk.calc.aucpext

Calculate the AUC percent extrapolated
print.PKNCAdata

Print a PKNCAdata object
pk.calc.ctrough

Determine the trough (predose) concentration
pk.calc.cmax

Determine maximum observed PK concentration
pk.calc.tlast

Determine time of last observed concentration above the limit of quantification.
pk.calc.tmax

Determine time of maximum observed PK concentration
pk.nca.interval

Compute all PK parameters for a single concentration-time data set
getDataName.PKNCAconc

Get the name of the element containing the data for the current object.
getDepVar

Get the dependent variable (left hand side of the formula) from a PKNCA object.
pk.tss

Compute the time to steady-state (tss)
exclude_nca

Exclude NCA parameters based on examining the parameter set.
print.provenance

Print the summary of a provenance object
pk.calc.aucint

Calculate the AUC over an interval with interpolation and/or extrapolation of concentrations for the beginning and end of the interval.
pk.calc.ae

Calculate amount excreted (typically in urine or feces)
pk.calc.auxc

A compute the Area Under the (Moment) Curve
roundString

Round a value to a defined number of digits printing out trailing zeros, if applicable.
setExcludeColumn

Set the exclude parameter on an object
find.tau

Find the repeating interval within a vector of doses
pk.calc.f

Calculate the absolute (or relative) bioavailability
roundingSummarize

During the summarization of PKNCAresults, do the rounding of values based on the instructions given.
setRoute

Set the dosing route
pk.calc.mrt

Calculate the mean residence time (MRT) for single-dose data or linear multiple-dose data.
pk.calc.mrt.md

Calculate the mean residence time (MRT) for multiple-dose data with nonlinear kinetics.
pk.calc.fe

Calculate fraction excreted (typically in urine or feces)
pk.tss.monoexponential.individual

A helper function to estimate individual and single outputs for monoexponential time to steady-state.
setAttributeColumn

Add an attribute to an object where the attribute is added as a name to the names of the object.
pk.tss.monoexponential.population

A helper function to estimate population and popind outputs for monoexponential time to steady-state.
PKNCA

Compute noncompartmental pharmacokinetics
setDuration

Set the duration of dosing or measurement
adj.r.squared

Calculate the adjusted r-squared value
get.first.model

Get the first model from a list of models
get.interval.cols

Get the columns that can be used in an interval specification
pk.calc.vd

Calculate the volume of distribution (Vd) or observed volume of distribution (Vd/F)
pk.calc.vss

Calculate the steady-state volume of distribution (Vss)
getGroups.PKNCAconc

Get the groups (right hand side after the | from a PKNCA object).
getIndepVar

Get the independent variable (right hand side of the formula) from a PKNCA object.
pk.calc.half.life

Compute the half-life and associated parameters
pk.calc.tlag

Determine the observed lag time (time before the first concentration above the limit of quantification or above the first concentration in the interval)
pk.calc.thalf.eff

Calculate the effective half-life
pk.calc.kel

Calculate the elimination rate (Kel)
summary.PKNCAresults

Summarize PKNCA results
pk.tss.stepwise.linear

Compute the time to steady state using stepwise test of linear trend
superposition

Compute noncompartmental superposition for repeated dosing
print.PKNCAconc

Print and/or summarize a PKNCAconc or PKNCAdose object.
as.data.frame.PKNCAresults

Extract the parameter results from a PKNCAresults and return them as a data frame.
sort.interval.cols

Sort the interval columns by dependencies.
split.PKNCAconc

Divide into groups
signifString

Round a value to a defined number of significant digits printing out trailing zeros, if applicable.
summary.PKNCAdata

Summarize a PKNCAdata object showing important details about the concentration, dosing, and interval information.
clean.conc.na

Handle NA values in the concentration measurements as requested by the user.
exclude

Exclude data points or results from calculations or summarization.
normalize_exclude

Normalize the exclude column by setting blanks to NA
model.frame.PKNCAconc

Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.
pk.calc.ceoi

Determine the concentration at the end of infusion
pk.calc.cl

Calculate the (observed oral) clearance
pk.calc.swing

Determine the PK swing
pk.tss.data.prep

Clean up the time to steady-state parameters and return a data frame for use by the tss calculators.
pk.tss.monoexponential

Compute the time to steady state using nonlinear, mixed-effects modeling of trough concentrations.
pk.calc.ptr

Determine the peak-to-trough ratio
print.summary_PKNCAresults

Print the results summary
reexports

Objects exported from other packages
time_calc

Times relative to an event (typically dosing)
tss.monoexponential.generate.formula

A helper function to generate the formula and starting values for the parameters in monoexponential models.