Title: Inference and Prediction of Generic Physiologically-Based Kinetic Models
Version: 0.2.5
BugReports: https://gitlab.in2p3.fr/mosaic-software/rPBK/-/issues
URL: https://gitlab.in2p3.fr/mosaic-software/rPBK/
Description: Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the 'rPBK' package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Biarch: true
Depends: R (≥ 3.4.0)
Imports: ggplot2, methods, Rcpp (≥ 0.12.0), rstan (≥ 2.26.0), rstantools
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
SystemRequirements: GNU make
Suggests: knitr, loo, rmarkdown, testthat
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: true
NeedsCompilation: yes
Packaged: 2025-06-12 09:54:32 UTC; virgile
Author: Virgile Baudrot [aut, cre], Sandrine Charles [aut], Christelle Lopes [aut], Ophélia Gestin [ctb], Dominique Lamonica [ctb], Aurélie Siberchicot [ctb]
Maintainer: Virgile Baudrot <virgile.baudrot@qonfluens.com>
Repository: CRAN
Date/Publication: 2025-06-12 11:10:01 UTC

The 'rPBK' package.

Description

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments

References

Stan Development Team (2022). RStan: the R interface to Stan. R package version 2.21.5. https://mc-stan.org

An example data set with 4 compartment

Description

A dataset containing an example with 4 compartments.

Usage

data(dataCompartment4)

Format

A data frame with 21 rows and 7 variables:

temps

vector of time

condition

exposure concentration

replicat

replicate of experiment

intestin

compartiment 'intestin'

caecum

compartiment 'caecum'

cephalon

compartiment 'cephalon'

reste

compartiment 'reste'

#'

An example data set with 1 compartment

Description

A dataset containing an example with a single compartment.

Usage

data(dataMaleGammarusSingle)

Format

A data frame with 22 rows and 4 variables:

time

vector of time

expw

exposure concentration

replicate

replicat of experiment

conc

internal measured concentration

#'

Create a list giving data and parameters to use in the model inference.

Description

Create a list giving data and parameters to use in the model inference.

Usage

dataPBK(object, ...)

## S3 method for class 'data.frame'
dataPBK(
  object,
  col_time = NA,
  col_replicate = NA,
  col_exposure = NA,
  col_compartment = NA,
  time_accumulation = NA,
  ku_nest = NA,
  ke_nest = NA,
  k_nest = NA,
  ...
)

nested_model(object)

## S3 method for class 'stanPBKdata'
nested_model(object)

Arguments

object

An object of class stanPBKdata (from dataPBK() function.

...

Further arguments to be passed to generic methods

col_time

Column name of the time column

col_replicate

Column name of the replicate column

col_exposure

Column name of the exposure column.

col_compartment

Column names of the compartment column. If several columns, give a vector with the column names.

time_accumulation

A scalar giving accumulation time.

ku_nest

Vector of binary (0 or 1) to select the uptake route. Use the nested_model() on the stanPBKdata object to check it.

ke_nest

Vector of binary (0 or 1) to select the excretion route. Use the nested_model() on the stanPBKdata object to check it.

k_nest

Matrix of binary (0 or 1) to select interaction routes. Use the nested_model() on the stanPBKdata object to check it.

Value

A list with data and parameters require for model inference.

Examples

# (1) load data file
data("dataCompartment4")
# (2) prepare data set
dataPBK_C4 <- dataPBK(
  object = dataCompartment4,
  col_time = "temps",
  col_replicate = "replicat",
  col_exposure = "condition",
  col_compartment = c("intestin", "reste", "caecum", "cephalon"),
  time_accumulation = 7)

# (1) load data file
data("dataCompartment4")
# (2) prepare data set
dataPBK_C4 <- dataPBK(
  object = dataCompartment4,
  col_time = "temps",
  col_replicate = "replicat",
  col_exposure = "condition",
  col_compartment = c("intestin", "reste", "caecum", "cephalon"),
  time_accumulation = 7)
# (3) check nesting
nested_model(dataPBK_C4)
# (2bis)
dataPBK_C42 <- dataPBK(
  object = dataCompartment4,
  col_time = "temps",
  col_replicate = "replicat",
  col_exposure = "condition",
  col_compartment = c("intestin", "reste", "caecum", "cephalon"),
  time_accumulation = 7,
  ku_nest = c(1,1,1,1), # No Change here
  ke_nest = c(1,1,1,1), # No Change here
  k_nest = matrix(c(
            c(0,1,1,1),
            c(0,0,1,1),
            c(0,0,0,0),
            c(0,0,0,0)),
            ncol=4,nrow=4,byrow=TRUE) # Remove 
  )
# (3bis) re-checking nesting
nested_model(dataPBK_C42)

Compute 95 credible intervals

Description

Compute quantiles 95 credible intervals

Usage

df_quant95_(x, ...)

Arguments

x

An object of class fitPBK

...

Additional arguments

Value

An object of class data.frame returning median and 95 credible interval

Interpolate function implemented in Stan only export for checking

Description

This function export the linear iterpolation implemented in Stan. It can be use to re-sample the exposure profiles.

Usage

export_interpolate(x, xpt, ypt, chain = 1, iter = 1, ...)

Arguments

x

interpolation point x

xpt

a vector of x axis (has to be same size as ypt vector)

ypt

a vector of y axis (has to be same size as ypt vector)

chain

number of chain

iter

number of iteration

...

Arguments passed to rstan::sampling

Value

A sample of a stanfit object returning a linear interpolation

Bayesian inference of TK model with Stan

Description

Bayesian inference of TK model with Stan

Usage

fitPBK(stanPBKdata, ...)

## S3 method for class 'stanPBKdata'
fitPBK(stanPBKdata, ...)

Arguments

stanPBKdata

List of Data require for computing

...

Arguments passed to rstan::sampling (e.g. iter, chains).

Value

An object of class fitPBK containing two object: stanPBKdata the data set used for inference and stanfit returned by rstan::sampling

Examples

# (1) load data file
data("dataCompartment4")
# (2) prepare data set
dataPBK_C4 <- dataPBK(
  object = dataCompartment4,
  col_time = "temps",
  col_replicate = "replicat",
  col_exposure = "condition",
  col_compartment = c("intestin", "reste", "caecum", "cephalon"),
  time_accumulation = 7)
# (3) run Bayesian fitting: <5 sec to be executed
# 1 chain and 10 iterations is fast to run but provide
# bad goodness-of-fit
fitPBK_C4_FASTbadGOF <- fitPBK(dataPBK_C4, chains = 1, iter = 10)

  # (3) run Bayesian fitting: > 5 sec to be executed
  # 4 chains and 2000 iterations provides better estimates
  fitPBK_C4 <- fitPBK(dataPBK_C4, chains = 4, iter = 2000)

An example of fitPBK object

Description

A fitPBK object containing Bayesian inference of the dataCompartment4 data set.

Usage

data(fitPBK_C4)

Format

A fitPBK object with:

stanPBKdata

original data frame wrap in a formatted list for inference

stanfit

a stanfit object resulting from inference with stan

Plotting method for fitPBK objects

Description

This is the generic plot S3 method for the fitTK. It plots the fit obtained for each variable in the original dataset.

Usage

## S3 method for class 'fitPBK'
plot(x, ...)

Arguments

x

And object returned by fitPBK

...

Additional arguments

Value

a plot of class ggplot

Examples

# (1) load a fitPBK object
data("fitPBK_C4")
# (2) plot result of bayesian fitting
plot(fitPBK_C4)

Posterior predictive check plot

Description

Plots posterior predictive check for fitPBK

Usage

ppc(x, ...)

## S3 method for class 'fitPBK'
ppc(x, ...)

Arguments

x

an object used to select a method ppc

...

Further arguments to be passed to generic methods

Value

a plot of class ggplot

Examples

# (1) load a fitPBK object
data("fitPBK_C4")
# (2) plot ppc of bayesian fitting
ppc(fitPBK_C4)

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