For this exercise, we’ll need the `campsismod`

package.
This package can be loaded as follows:

`library(campsismod)`

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`

…

`<- read.campsis("resources/minimalist_model/") model `

```
## 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.

`<- CampsisModel() model `

Then, let’s define the equation of our model parameter
`K`

.

`<- 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 `THETA_K`

and
`ETA_K`

:

```
<- 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")
```