README.md

In nanhung/pksensi: Global Sensitivity Analysis in Physiologically Based Kinetic Modeling

pksensi

pksensi implements the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters.

Through pksensi, you can:

• Run sensitivity analysis for PK models in R with script that were written in C or GNU MCSim.

• Decision support: The output results and visualization tools can be used to easily determine which parameters have “non-influential” effects on the model output and can be fixed in following model calibration.

Installation

You can install the released version of pksensi from CRAN with:

install.packages("pksensi")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("nanhung/pksensi")

• This package includes a function that can help you install GNU MCSim more easily through the function mcsim_install().

• All updated details can be found in NEWS.md.

• NOTE: Windows users need to install Rtools40 to compile the model code.

Workflow

Note: The parameter correlation (e.g., Vmax and KM in metabolism) might be an issue in the global sensitivity analysis. If you have experiment data, suggest using small datasets as a sample in Markov Chain Monte Carlo Simulation. Then, check correlation before conducting the sensitivity analysis. The issue will try to address in the future version.

Example

This is a basic example of applying pksensi in one-compartment pbtk model:

library(pksensi)

Step 1. Construct 1-cpt pbtk model

pbtk1cpt <- function(t, state, parameters) {
  with(as.list(c(state, parameters)), {
    dAgutlument = - kgutabs * Agutlument
    dAcompartment = kgutabs * Agutlument - ke * Acompartment
    dAmetabolized = ke * Acompartment
    Ccompartment = Acompartment / vdist * BW;
    list(c(dAgutlument, dAcompartment, dAmetabolized), 
         "Ccompartment" = Ccompartment) 
  })
}

Step 2. Define initial conditions, output time steps and variable

initState <- c(Agutlument = 10, Acompartment = 0, Ametabolized = 0)
t <- seq(from = 0.01, to = 24.01, by = 1)
outputs <- c("Ccompartment")

Step 3. Generate parameter matrix

3.1. (Optional) Extract parameter value from httk package

library(httk)
pars1comp <- (parameterize_1comp(chem.name = "acetaminophen"))
#> Human volume of distribution returned in units of L/kg BW.

3.2. Set parameter distributions

q <- c("qunif", "qunif", "qunif", "qnorm")
q.arg <- list(list(min = pars1comp$Vdist / 2, max = pars1comp$Vdist * 2),
              list(min = pars1comp$kelim / 2, max = pars1comp$kelim * 2),
              list(min = pars1comp$kgutabs / 2, max = pars1comp$kgutabs * 2),
              list(mean = pars1comp$BW, sd = 5))

3.3. Create parameter matrix

set.seed(1234)
params <- c("vdist", "ke", "kgutabs", "BW")
x <- rfast99(params, n = 200, q = q, q.arg = q.arg, replicate = 1)

Step 4. Conduct simulation (will take few minutes with more replications)

out <- solve_fun(x, time = t, func = pbtk1cpt, initState = initState, outnames = outputs)
#> Starting time: 2024-11-27 07:07:16.85445
#> Ending time: 2024-11-27 07:07:25.711756

Step 5. Uncertainty analysis

pksim(out)  # Use to compare with "real" data (if any)

Step 6. Check and visualize the result of sensitivity analysis

plot(out)   # Visualize result

check(out)  # Print result to console
#> 
#> Sensitivity check ( Index > 0.05 )
#> ----------------------------------
#> First order:
#>  vdist ke kgutabs 
#> 
#> Interaction:
#>  vdist ke kgutabs 
#> 
#> Total order:
#>  vdist ke kgutabs 
#> 
#> Unselected factors in total order:
#>  BW 
#> 
#> 
#> Convergence check ( Index > 0.05 )
#> ----------------------------------
#> First order:
#>   
#> 
#> Interaction:
#>   
#> 
#> Total order:
#> 

Citation

To cite pksensi in publications use:

  Hsieh, N-H., Reisfeld B., and Chiu W.A., (2020). pksensi: An R
  package to apply global sensitivity analysis in physiologically based
  kinetic modeling SoftwareX, 12, 100609.
  https://doi.org/10.1016/j.softx.2020.100609

A BibTeX entry for LaTeX users is

  @Article{,
    title = {{pksensi}: An R package to apply global sensitivity analysis in physiologically based kinetic modeling},
    author = {Nan-Hung Hsieh and Brad Reisfeld and Weihsueh A. Chiu},
    journal = {SoftwareX},
    year = {2020},
    volume = {12},
    pages = {100609},
    doi = {10.1016/j.softx.2020.100609},
  }

Reference

Hsieh NH, Reisfeld B, Bois FY, Chiu WA. Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic modeling. Frontiers in Pharmacology 2018 Jun; 9:588.

Hsieh NH, Reisfeld B, Chiu WA. pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling. SoftwareX 2020 Jul; 12:100609.

Hsieh NH, Bois FY, Tsakalozou E, Ni Z, Yoon M, Sun W, Klein M, Reisfeld B, Chiu WA. A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms. Journal of Pharmacokinetics and Pharmacodynamics 2021 Sep; 22:1-6.

nanhung/pksensi documentation built on Dec. 2, 2024, 3:52 a.m.