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For this exercise, we’ll need the campsismod
package.
This package can be loaded as follows:
Assume a very simple 1-compartment PK model with first-order
eliminate rate 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 K
:
This model can now be loaded by campsismod
…
## Warning in read.allparameters(folder = folder): No file 'sigma.csv' could be
## found.
Let’s simulated this model in CAMPSIS:
library(campsis)
dataset <- Dataset(25) %>% add(Observations(seq(0,24,by=0.5)))
results <- model %>% simulate(dataset=dataset, seed=1)
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
K
.
We can add an ordinary differential equation as follows:
We can init the central compartment as well on the fly:
Finally, let’s define our THETA_K
and
ETA_K
:
model <- model %>% add(Theta("K", value=0.06))
model <- model %>% add(Omega("K", value=15, type="cv%"))
This model can simulated by CAMPSIS as well. Powerful, isn’t it?
library(campsis)
dataset <- Dataset(25) %>% add(Observations(seq(0,24,by=0.5)))
results <- model %>% simulate(dataset=dataset, seed=2)
spaghettiPlot(results, "A_CENTRAL")
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They may not be fully stable and should be used with caution. We make no claims about them.
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