• 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.


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


Initial values


Convergence plots

Estimated parameters