What's new in MonolixSuite 2023

Very intuitive interface & automatization via scripting

Designed with a user-friendly graphical interface. Intuitive settings panels, clear definitions of methods, visual tools to check the calculations and personalized display of results – all contribute to a better productivity. Accessible also via R for powerful scripting.

Straightforward NCA and Bioequivalence Studies

Complete workflow – integrated calculation of NCA parameters with industry-standard methods and bioequivalence study as recommended by regulatory agencies – is done in a few clicks. NCA analysis has never been so simple.

Reliable and clear results

Intuitive tables, sortable summaries and interactive plots provide a powerful environment for analysis of results. Reproducibility, correct installation and calculations are guaranteed by settings saved in the project and the integrated validation suite.

Compartmental analysis & integration towards population modeling

Built-in library of PK models and automatic features help to describe PK dynamics within the Compartmental Analysis framework. Together with a direct link towards population modeling using Monolix assure the most informative workflow.

03. PKanalix WHAT & HOW
Novel Approaches

Data exploration

Perform a first quality-check of your data and get an overview of the full trial in a glimpse with automatically generated, interactive, fully customizable plots covering PK dynamics and relations between covariates.

Non Compartmental analysis of PK datasets

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

This task requires defining rules for the calculation of the λz(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.

Bioequivalence study

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.

Compartmental Analysis of PK datasets

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.

This task defines a structural model (based on a user-friendly PK models library) and estimates the parameters for all the individuals. Automatic initialization method improves the convergence of parameters for each individual.

Outputs and plots

All NCA and/or CA outputs are displayed in sortable formatted tables easy to copy to any document, and exported in the result folder in an R-compatible format. Interactive plots are also generated 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.

Comprehensive documentation and examples

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.

A lot of online material (feature of the weeks, webinars, …) on our Lixoft University page.

PKanalix has been widely used by pharmaceutical companies and academics to:

  • Fulfill regulatory submissions with reliable NCA parameters calculation, following all standards of regulatory agencies.
  • Analyse multiple candidate drugs in phase I to find the most promising ones.
  • Prepare future population modeling steps by checking the impact of covariates and dose on NCA parameters, and finding a first PK model to start with in the compartmental analysis.
  • Design the next clinical trial by post-processing their simulations in Simulx with their preferred NCA parameters.
  • Investigate generic formulations, food effects or drug interactions in the bioequivalence studies.




Global interoperability

Extended interoperability with export between all MonolixSuite applications. In addition to the exports/imports already available, it is now possible to:

  • Export a PKanalix CA run to Simulx to predict PK for new dosing regimens.
  • Export simulations from Simulx, and individual fits or VPC simulations from Monolix, to PKanalix for NCA analysis.

Report generation

Automatic generation of a report from the GUI with result tables and plots:

  • Default report: default results tables and plots with settings, stratification and preferences customized by a user in the GUI.
  • Custom report: full customization of tables and plots.
  • Reports templates

Generalized CA

  • Compartmental analysis for any model: access to full model libraries and custom models
  • Improves optimization algorithm
  • New weighting options in the objective function
  • Individual and global cost functions
  • Observations vs predictions diagnostic plot

Global display

  • New normalization units
  • Custom axis limits for Observed data
  • Multiple trendlines (mean and standard error) for different covariate groups in one chart for Observed data plot.
  • Global aliases for NCA parameters
  • Feedback on the software directly from the GUI

Data formatting

A data formatting module, integrated in the GUI, to adapt a dataset to the Monolix input format.

  • Merging of several observation types
  • Add censoring information
  • Definition of a treatment
  • Adding additional columns from an external file




04. PKanalix Experts
Meet The Experts

How do I move forward from here?

Request for a demo with Monolix to support internal research projects and regulatory interactions.