• 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.

• 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