For this exercise, we’ll need the
This package can be loaded as follows:
Assume a very simple 1-compartment PK model with first-order
K. Say this parameter has a typical value of
log(2)/12≈0.06 (where 12 is the elimination half life) and has 15% CV.
Let’s also initiate the central compartment to 1000.
This can be translated into the following CAMPSIS model ( download Notepad++ plugin for CAMPSIS ):
Let’s now create our
theta.csv with our single parameter
K as follows:
And finally, let’s also create our
omega.csv to include
inter-individual variability on
This model can now be loaded by
## Warning in read.allparameters(folder = folder): No file 'sigma.csv' could be ## found.
Let’s simulated this model in CAMPSIS:
library(campsis) <- Dataset(25) %>% add(Observations(seq(0,24,by=0.5))) dataset <- model %>% simulate(dataset=dataset, seed=1) results spaghettiPlot(results, "A_CENTRAL")
The same model can be created programmatically. First, let’s create an empty CAMPSIS model.
Then, let’s define the equation of our model parameter
<- model %>% add(Equation("K", "THETA_K*exp(ETA_K)"))model
We can add an ordinary differential equation as follows:
<- model %>% add(Ode("A_CENTRAL", "-K*A_CENTRAL"))model
We can init the central compartment as well on the fly:
<- model %>% add(InitialCondition(compartment=1, "1000"))model
Finally, let’s define our
<- model %>% add(Theta("K", value=0.06)) model <- model %>% add(Omega("K", value=15, type="cv%"))model
This model can simulated by CAMPSIS as well. Powerful, isn’t it?
library(campsis) <- Dataset(25) %>% add(Observations(seq(0,24,by=0.5))) dataset <- model %>% simulate(dataset=dataset, seed=2) results spaghettiPlot(results, "A_CENTRAL")