Use the menu

`Plot`

to plot the PK profile, modify the PK model, the PK parameters, the dosage regimen and the design of the output.Use the menu

`Table`

to create and save a table with the concentration values.Use the menu

`Codes`

to display and download the`Simulx`

R code`pkmodelCode.R`automatically generated using the current settings, the`ui.R`and`server.R`files used for this application.

Select the administration route and the dosage regimen in the menu **Administration**:

- iv bolus, iv infusion, or oral administration,
- time of first dose,
- number of doses,
- interdose interval,
- amount.
- infusion time (only for infusion),

Select the parameterization in the menu **Parameterization**:

- rate constants (\(k\), \(k_{12}\), \(k_{21}\), \(k_{13}\), \(k_{31}\)),
- clearances (\(Cl\), \(Q_{2}\), \(V_{2}\), \(Q_{3}\), \(V_{3}\)),

Define the PK model in the menu **Model**:

- Define the absorption process for oral administration in the submenu
*Absorption*:- zero-order or first-order absorption process with the associated paramameters (duration of absorption \(Tk0\) or absorption rate constant \(ka\))
- lag time \(Tlag\)
- transit compartment model with mean transit time \(Mtt\) and number of transit compartments \(Ntr\) (only for first-order absorption)

- Define the distribution process in the submenu
*Distribution*:- 1, 2 or 3 compartments
- volume of distribution \(V\)
- transfer rates constants \(k_{12}\) and \(k_{21}\) between compartments 1 and 2
- transfer rates constants \(k_{13}\) and \(k_{31}\) between compartments 1 and 3

- Define the elimination process in the submenu
*Elimination*:- linear or Michaelis Menten elimination process
- elimination rate constant \(k\), or
- Michaelis Menten parameters \(V_m\) and \(K_m\).

Define the output in the menu **Output**:

- select the time range where the predicted concentration is computed,
- select the number of time points of the grid where the predicted concentration is computed,
- choose between linear and semi-log scale.

### Model

#### Structural model:

1 compartment PK model with first order absorption and linear elimination.

parameters = (ka, V, Cl)

#### Statistical model:

- lognormal distributions on (ka, V, Cl)

- log(volume) = linear function of log(weight):

log(V_i) = log(V_pop) + beta log(w_i/70) + eta_(V,i)

- constant residual error: y = f + ae