PKanalix is a user friendly and fast application for compartmental and non-compartmental analysis (NCA)

Very intuitive interface

PKanalix can be used via a graphical interface to easily define settings and rules, check the calculations and display the results. It can also be used via R for powerful scripting.

Straightforward NCA

PKanalix calculates NCA parameters with industry-standard methods, and automatically generates a full set of plots to visualize the data, the distributions of calculated parameters and correlations with covariates.

Reliable and clear results

All results are available as tables and summaries, and as interactive plots for a fast and intuitive interpretation. All settings are saved in the project for reproducible results, and the integrated validation suite ensures correct installation and calculation.

Compartmental analysis

Calculation of the parameters in the Compartmental Analysis framework is also proposed with a large library of PK models. In addition, it includes a direct link toward population modeling using Monolix.

Key features

Non Compartmental analysis

The first main feature of PKanalix is the calculation of the parameters in the Non Compartmental Analysis framework.

This task consists in defining rules for the calculation of the Lambda_z (slope of the terminal elimination phase) to be able to compute the NCA parameters. This definition can be done either globally via rules (e.g adjusted R2 or time window) or on each individual where the user can choose or remove any point for the calculation.

Compartmental Analysis

The second main feature of PKanalix is the calculation of the parameters in the Compartmental Analysis framework. It consists in finding parameters of a PK model representing the kinetics in compartments for each individual. Automatic initialization is performed for a better convergence of each parameter for each individual.

Bioequivalence Analysis

The average NCA parameters obtained for different groups (e.g a test and a reference formulation) can be compared using the Bioequivalence task. Linear model definition contains one or several fixed effects selected in an integrated module. It allows to obtain a confidence interval compared to the predefined BE limits and automatically displayed in intuitive tables and plots.

Outputs and plots

All NCA, CA and Bioequivalence outputs are displayed in sortable tables and exported in the result folder in a R-compatible format. Interactive plots are also proposed for straightforward interpretation of the results.

R API to automate your process

All steps performed in PKanalix can be run from R with the LixoftConnectors package. What you have done once intuitively in the interface on a specific dataset can be generalized to a script automating the process for any other dataset.

Integration of your NCA/CA projects to the Modeling & Simulation & Trial Design workflow

Use the same data file for all your analyses – Data exploration, NCA, CA, Population Modeling. PKanalix alone does not include population analysis. However, exporting your PKanalix projects to Monolix will speed up your population modeling since it automatically sets everything you need in Monolix to run parameter estimation in a population modeling framework in a click: interpreted dataset, structural model, initial values do not need to be redefined. Importing formatted datasets from clinical trials simulated in Simulx is also possible to post-process your outputs and analyse trials with your favorite NCA parameters.

New features in 2021

  • Additional results validity check with a fingerprint of a dataset – detection of any changes in a dataset when a project is reloaded.
  • Bioequivalence module integrated with the NCA workflow – comparison of the NCA parameters of several drug products (usually two, called ‘test’ and ‘reference’) using the average bioequivalence.
  • More flexibility: more options for means and sd, NCA individual fits plot, new settings for axis, and re-ordering of stratified plots.
  • Improved results display: stratification options for the summary tables in NCA and CA
  • New R-functions to generate plots as ggplot objects
  • New interface features: dark theme, font size, choice of significant digits.


Great care has been taken to provide the user with a comprehensive PKanalix documentation that includes methodology, software manuals and tutorials. A wide collection of examples that include models and data can be used as templates to start your own project.

The format of the data set is shared across all applications of the MonolixSuite. The main rules to format a data set for PKanalix are presented on the PKanalix documentation. A general web site for the data set formatting for PKanalix and Monolix is also proposed here.


Jonathan CHAUVINPKanalix