Optimal environment to build and analyze simulation scenarios

Definition – create easily new exploration and simulation elements (parameters, treatments, outputs, covariates, etc.) of different types using built-in methods or external tables Exploration – analyze in real time different treatments and effects of model parameters by simulating a typical individual; create several exploration groups, overlay experimental data and send a scenario in a single click to a clinical trial simulation Simulation – simulate clinical trials using a population of individuals in one or several groups with specific treatment, individual characteristics or measurement times; use flexible post-processing tools, and get immediate feedback in intuitive exportable tables and interactive plots.

Outputs and plots

All simulation outputs are displayed in sortable and formatted tables easy to copy in any document and are exported in the result folder in an R-compatible format. Interactive plots are also automatically generated for straightforward interpretation of the results.

R API to automate your process

All steps performed in Simulx can be run from R with the LixoftConnectors package. What you have done once intuitively in the interface in a specific scenario can be generalized to a script automating the process for any other simulation or design optimization.

Comprehensive documentation and examples

Great care has been taken to provide a comprehensive Simulx 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 videos, webinars, case studies, …) on our Lixoft University page.

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

• Make more informed decisions with reliable and extended analysis of possible scenarios.
• Compare different dosing regimens and find the most promising ones in terms of safety and efficiency.
• Assess the uncertainty in a clinical trial, calculate the expected power of a study, and select the most successful design.
• Use results of one phase of a clinical trial to optimize treatment, sample size and duration of the next phase.

• Uncertainty of population parameters captured in the variance-covariance matrix of the estimates that has been calculated in Monolix during the “standard errors” task. It allows to propagate the uncertainty of the estimated population parameters to the predictions.
• More flexibility in the exploration plots: possibility to overlay experimental data on top of model predictions, to set the axis limits and log scale on each subplot independently, and new sliders to change parameters values – all contribute to better user experience and more efficient analysis.
• Export of simulated data as MonolixSuite formatted dataset in one click to easily perform a complete modeling workflow in PKanalix or Monolix.
• Integrated model editor with built in libraries, syntax check, auto competition and zoom – writing and editing models have never been so easy.
• and many more!

Check a short overview of new Simulx 2021 features or watch how the MonolixSuite 2021 works in practice on a parent-metabolite case study.