Pirana Modeling Workbench

Organizing and managing pharmacometric models can be an inefficient and time-consuming process. Pirana’s workbench tools provide modelers with structure to facilitate the iterative processes used to create population PK/PD models and perform simulations resulting in better organization and more efficient analysis of population PK/PD results.

The Pirana Modeling Workbench includes:

  • Model templates and wizards
  • Library of goodness-of-fit plots (R)
  • Model translation tools
  • Built-in support for version control and audit trails
  • Seamless access to open source tools to interface with pop PK/PD modeling tools for visual predictive check, covariate modeling, and bootstraps

Pirana works with R speaks NLME, NONMEM, R, Perl Speaks NONMEM (PSN), and Monolix. Pirana is supported on Windows and Linux operating systems.

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Organize and manage models

Analogous to a lab notebook, Pirana creates the necessary infrastructure to manage, create, track, and perform simulations.

It delivers faster, more efficient modeling and simulation by connecting multiple tools for optimizing a pharmacometrics workflow.

Pirana provides an automated folder structure for the storage of analytical results, adding order and traceability to complex analyses.

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Organize and manage models
Analyze data and generate reports

Analyze data and generate reports

Pirana offers a quick and easy graphical interface to R plotting tools and supports the generation of plots in R using PSN and Xpose or custom user scripts.

Our powerful reporting tools provide options to run turnkey reports in various formats, including Word, html, plain text, LaTeX, and Excel.

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Visualize the model decision tree

The Visual Run Record in Pirana documents the model development process through a unique interactive visualization.

A summary of the model selection thought process displays how models were evaluated, and how and why the final model was chosen.

Pirana also provides fast and easy model evaluation and side-by-side model diagnostics to aid in the model development process.

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Visualize the model decision tree