Pirana and Integrated PMX Tools, a Workbench for NONMEM, NLME, pyDarwin, and RsNLME

Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include: Integration of the NLME engine via the R speaks NLME (RsNLME) package for developing NLME models Integration of a Shiny graphical interface for the construction of NLME models Integration of machine learning pyDarwin python package Integration of a Shiny interface for custom diagnostics including Goodness of Fit (GOF) plots, tables, Visual Predictive Check (VPC) and report shell generation Improved setup with support for parallel execution on a wide range of platforms In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.