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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, type the following commands:

install.packages("remotes")
remotes::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
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, 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

2,409

Version

0.10.2

License

AGPL-3

Issues

51

Pull Requests

14

Stars

80

Forks

29

Maintainer

Bill Denney

Last Published

April 29th, 2023

Functions in PKNCA (0.10.2)

adj.r.squared

Calculate the adjusted r-squared value
check.conc.time

Verify that the concentration and time are valid
check.interval.specification

Check the formatting of a calculation interval specification data frame.
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.
clean.conc.blq

Handle BLQ values in the concentration measurements as requested by the user.
choose_interp_extrap_method

Choose a method for calculation in the interval between concentrations
cov_holder

Calculate the covariance for two time points with sparse sampling
filter.PKNCAresults

dplyr filtering for PKNCA
find.tau

Find the repeating interval within a vector of doses
clean.conc.na

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

Exclude NCA parameters based on examining the parameter set.
exclude

Exclude data points or results from calculations or summarization.
check.conversion

Check that the conversion to a data type does not change the number of NA values
get.best.model

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

Add a hash and associated information to enable checking object provenance.
checkProvenance

Check the hash of an object to confirm its provenance.
choose.auc.intervals

Choose intervals to compute AUCs from time and dosing information
formula.PKNCAconc

Extract the formula from a PKNCAconc object.
getDataName.PKNCAconc

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

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

Get the columns that can be used in an interval specification
geomean

Compute the geometric mean, sd, and CV
getDepVar

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

dplyr grouping for PKNCA
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.
get.parameter.deps

Get all columns that depend on a parameter
interp.extrap.conc

Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.
inner_join.PKNCAresults

dplyr joins for PKNCA
group_vars.PKNCAconc

Get grouping variables for a PKNCA object
interp_extrap_conc_method

Interpolate or extrapolate concentrations using the provided method
get.first.model

Get the first model from a list of models
getGroups.PKNCAconc

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

Is a PKNCA object used for sparse PK?
pk.calc.auxc

A compute the Area Under the (Moment) Curve
pk.calc.cav

Calculate the average concentration during an interval.
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.
getAttributeColumn

Retrieve the value of an attribute column.
pk.calc.c0

Estimate the concentration at dosing time for an IV bolus dose.
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").
pk.calc.ae

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

Calculate the AUC above a given concentration
pk.calc.deg.fluc

Determine the degree of fluctuation
pk.calc.ceoi

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

Determine dose normalized NCA parameter
pk.calc.f

Calculate the absolute (or relative) bioavailability
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.fe

Calculate fraction excreted (typically in urine or feces)
pk.calc.cstart

Determine the concentration at the beginning of the interval
parse_formula_to_cols

Convert a formula representation to the columns for input data
normalize_exclude

Normalize the exclude column by setting blanks to NA
pk.calc.ctrough

Determine the trough (end of interval) concentration
pk.calc.mrt.md

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

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

Determine the PK swing
getIndepVar

Get the independent variable (right hand side of the formula) from a PKNCA object.
mutate.PKNCAresults

dplyr mutate-based modification for PKNCA
model.frame.PKNCAconc

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

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

Determine the peak-to-trough ratio
pk.calc.sparse_auc

Calculate AUC and related parameters using sparse NCA methods
pk.calc.vz

Calculate the terminal volume of distribution (Vz)
pk.nca

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

Calculate the steady-state volume of distribution (Vss)
pk.calc.vd

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

Calculate the effective half-life
pk.tss.monoexponential.individual

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

Calculate AUC for intravenous dosing
pk.tss.monoexponential

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

Calculate renal clearance
pk.calc.clast.obs

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

Determine maximum observed PK concentration
roundingSummarize

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

Convert the grouping info and list of results for each group into a results data.frame
setAttributeColumn

Add an attribute to an object where the attribute is added as a name to the names of the object.
roundString

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

Set the duration of dosing or measurement
pknca_find_units_param

Find NCA parameters with a given unit type
print.PKNCAconc

Print and/or summarize a PKNCAconc or PKNCAdose object.
pk.tss

Compute the time to steady-state (tss)
setExcludeColumn

Set the exclude parameter on an object
pk.tss.data.prep

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

Calculate the AUC percent extrapolated
pknca_units_table

Create a unit assignment and conversion table
sparse_pk_attribute

Set or get a sparse_pk object attribute
setRoute

Set the dosing route
tss.monoexponential.generate.formula

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

Print a PKNCAdata object
pk.calc.half.life

Compute the half-life and associated parameters
var_sparse_auc

Calculate the variance for the AUC of sparsely sampled PK
pk.calc.tmax

Determine time of maximum observed PK concentration
pk.calc.kel

Calculate the elimination rate (Kel)
pk.calc.tlast

Determine time of last observed concentration above the limit of quantification.
pk.nca.interval

Compute all PK parameters for a single concentration-time data set
time_calc

Times relative to an event (typically dosing)
superposition

Compute noncompartmental superposition for repeated dosing
sparse_to_dense_pk

Extract the mean concentration-time profile as a data.frame
print.summary_PKNCAresults

Print the results summary
pk.nca.intervals

Compute NCA for multiple intervals
pk.tss.monoexponential.population

A helper function to estimate population and popind outputs for monoexponential time to steady-state.
pk.tss.stepwise.linear

Compute the time to steady state using stepwise test of linear trend
pk.calc.time_above

Determine time at or above a set value
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)
pknca_units_add_paren

Add parentheses to a unit value, if needed
sparse_auc_weight_linear

Calculate the weight for sparse AUC calculation with the linear-trapezoidal rule
sparse_mean

Calculate the mean concentration at all time points for use in sparse NCA calculations
print.provenance

Print the summary of a provenance object
signifString

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

Perform unit conversion (if possible) on PKNCA results
summary.PKNCAdata

Summarize a PKNCAdata object showing important details about the concentration, dosing, and interval information.
sort.interval.cols

Sort the interval columns by dependencies.
reexports

Objects exported from other packages
summary.PKNCAresults

Summarize PKNCA results
PKNCAconc

Create a PKNCAconc object
PKNCAdata

Create a PKNCAdata object.
PKNCA.options

Set default options for PKNCA functions
PKNCA

Compute noncompartmental pharmacokinetics
PKNCA_impute_method

Methods for imputation of data with PKNCA
PKNCA.options.describe

Describe a PKNCA.options option by name.
PKNCA.set.summary

Define how NCA parameters are summarized.
PKNCA.choose.option

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

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

Separate out a vector of PKNCA imputation methods into a list of functions
PKNCAdose

Create a PKNCAdose object
as.data.frame.PKNCAresults

Extract the parameter results from a PKNCAresults and return them as a data frame.
as_sparse_pk

Generate a sparse_pk object
PKNCAresults

Generate a PKNCAresults object
business.mean

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

Add columns for calculations within PKNCA intervals
add_impute_to_intervals

Add the imputation column to the intervals, if it is not already there
any_sparse_dense_in_interval

Determine if there are any sparse or dense calculations requested within an interval