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.
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.
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.