Tools

Tools

This is not an exhaustive list, but aims to cover most tools of interest, both commercial and free.

bbr: https://github.com/metrumresearchgroup/bbr

  • A user-friendly interface between R and NONMEM® to manage, track, and report modeling activities through simple R objects. Users can submit models, consume outputs and diagnostics, and iterate on models. bbr provides simple tagging and model inheritance trees to support the replication and external review of your work. bbr makes model development more efficient, reproducible, and traceable. 

bbr bayes: https://github.com/metrumresearchgroup/bbr.bayes

  • bbr.bayes, an extension of the bbr package, seamlessly integrates with Stan and NONMEM Bayesian estimation methods. It enables modelers to take advantage of advanced Bayesian modeling techniques, while leveraging bbr’s capabilities for model management, traceability, and reproducibility.

GNU Octave: gnu.org/software/octave

  • A free, but less fully-featured alternative to MATLAB.

Julia: julialang.org

  • Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.

lastdose: https://github.com/metrumresearchgroup/lastdose

  • Efficiently computes recent dose timing and amount within clinical trial datasets. Improves time since last dose calculations, outperforming manual methods. Designed for time-based data.

MATLAB: mathworks.com/products/matlab

  • A heavily-optimized platform for performing complex mathematical computing tasks.

MeRGE: https://www.metrumrg.com/merge-expo/

  • MeRGE is a suite of freely-available, open-source tools for traceable, reproducible, and scalable pharmacometric workflows.

Metworx: https://www.metrumrg.com/solution/metworx/

  • Metworx is a Platform-as-a-Service offering a powerful means for scientists to create reproducible models and run simulations. Metworx utilizes High Performance Computing and offers a computational environment with a variety of programming languages, and graphical visualization programs in a means that fosters collaboration and ensures scientists are working in a validated state. 

Monolix: simulations-plus.com/software/monolix/

  • Easy, fast and powerful tool for parameter estimation in non-linear mixed effect models, model diagnosis and assessment, and advanced graphical representation. Accompanied by supporting products Datxplore, Mlxplore and Simulx.

mrgda: https://github.com/metrumresearchgroup/mrgda

  • mrgda – Metrum Research Group Data Assembly – is a robust data programming tool engineered to seamlessly consolidate diverse clinical trial data into meticulously structured, analysis-ready datasets. mrgda accelerates the data assembly process, freeing up your valuable time for in-depth analysis and decision making. 

mrgsolve: https://github.com/metrumresearchgroup/mrgsolve

  • mrgsolve facilitates simulation in R from hierarchical, ordinary differential equation (ODE) based models typically employed in drug development. The modeler creates a model specification file consisting of R and C++ code that is parsed, compiled, and dynamically loaded into the R session. Input data are passed in and simulated data are returned as R objects, so disk access is never required during the simulation cycle after compiling. 

MPN (Metrum Package Network): https://kb.metworx.com/Users/Managing_R_Packages/r-package-management/

  • A curated, reproducible R package environment:

    • Open repository of ~1000 packages specific to PMX sourced from CRAN and github
    • Compatibility and stability tested
    • Binaries for Windows and MacOS across multiple versions of R
    • Immutable repository
    • Freely available and accessible to all

mpn.scorecard: https://github.com/metrumresearchgroup/mpn.scorecard

  • mpn.scorecard is an influential tool that aids in the evaluation of R packages for inclusion in the Metrum Package Network (MPN). By assessing key attributes and the risk associated with inclusion in the network, mpn.scorecard eliminates ambiguity and subjectivity, providing a clear and quantifiable assessment of a package’s reliability and quality.

nlmixr: nlmixr.org

  • nlmixr is an R package for fitting general dynamic models, pharmacokinetic (PK) models and pharmacokinetic-pharmacodynamic (PKPD) models in particular, with either individual data or population data. It’s free and open source, and will remain so.

nmrec: https://github.com/metrumresearchgroup/nmrec

  • An R package for reading, parsing, and modifying NONMEM control records. Its purpose is to support scripted and programmatic manipulation of mathematical model definitions, eliminating the need for manual, piece-by-piece adjustments. While not prominently used by end-users, it serves as a behind-the-scenes utility in several other packages, to support efficiently and reliably working with NONMEM.

NONMEM: iconplc.com/innovation/nonmem/

  • The gold standard software in Population Pharmacokinetic and Pharmacokinetic-Pharmacodynamic modelling.

Perl-speaks-NONMEM (PsN): github.io/PsN/

  • Perl-speaks-NONMEM (PsN) is a collection of Perl modules and programs aiding in the development of non-linear mixed effect models using NONMEM. The functionality ranges from solutions to simpler tasks such as parameter estimate extraction from output files, data file subsetting and resampling, to advanced computer-intensive statistical methods. PsN includes stand-alone tools for the end-user as well as development libraries for method developers.

Phoenix NLME: certara.com/software/pkpd-modeling-and-simulation/phoenix-nlme

  • Phoenix NLME software is a population modeling and simulation solution for scientists with all levels of experience—from the most advanced modelers to novice PK/PD scientists. This comprehensive package includes integrated data preparation, modeling, and graphics tools, and uses the same GUI as Phoenix WinNonlin.

Pirana: pirana-software.com

  • Pirana offers a powerful workbench for pharmacometric modeling, on your desktop or in the cloud.

pkgr: https://github.com/metrumresearchgroup/pkgr

  • pkgr is a rethinking of the way packages are managed in R. Namely, it embraces the declarative philosophy of defining an ideal state of the entire system and works towards achieving that objective. pkgr is built with a focus on reproducibility and auditability of what is going on, a vital component for the pharmaceutical sciences + enterprises.

pmforest: https://github.com/metrumresearchgroup/pmforest

  • Forest plots for pharmacometrics. pmforest is an R package to create forest plots, a visualization tool for identified variables of interest. 

pmparams: https://github.com/metrumresearchgroup/pmparams

  • pmparams enables users to extract, organize, and present parameter estimates with ease and clarity. This package streamlines the annotation and formatting of these tables–especially when integrated with bbr and pmtables–resulting in comprehensive reports capturing model nuances and accurate and easy-to-understand parameter estimate tables. 

pmplots: https://github.com/metrumresearchgroup/pmplots

  • Plots for pharmacometrics. pmplots provides light wrappers around ‘ggplot2’ plotting code tailored for pharmacometric applications.

PMXStan: github.com/yxiong1/pmxstan

  • PMXStan is an R package aiming at facilitating Stan-based modeling building, fitting, diagnosis, and simulations for pharmacometrics uses.

pmtables: https://github.com/metrumresearchgroup/pmtables

  • Tables for pharmacometrics. pmtables is an R package that allows you to summarize data sets and create publication-quality tables for inclusion in ‘tex’ documents

Pumas: https://pumas.ai

  • Pumas is a one-stop integrated modelling and simulation platform powered by the Julia language. It can perform non-compartmental analysis (NCA), nonlinear mixed-effects modelling (NLME), Bioequivalence (BE), in vitro-in vivo correlation (IVIVC) and clinical trial simulations (CTS). Further, Pumas provides convenient way of handling of multi-scale PBPK, QSP models. DeepNLME is the state-of-the-art product of Pumas that seamlessly integrates NLME and Deep Learning, via scientific machine learning. Pumas comes with a full suite of productivity tools to pre- and post-process your analyses in an interactive manner. Scientists can easily convert complex models to dashboards effortlessly to collaborate with interdisciplinary teams. Pumas was developed as a cloud-first technology that allows scientists scale to thousands of CPUs and GPUs with a single click. The software is free for non-commercial research and training..

R: r-project.org

  • R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Stan: mc-stan.org 

  • Stan provides a full Bayesian platform for statistical modeling, data analysis, and prediction. Users specify log density functions in Stan’s probabilistic programming language and get full Bayesian statistical inference with MCMC sampling (NUTS, HMC), approximate Bayesian inference with variational inference (ADVI), penalized maximum likelihood estimation with optimization (L-BFGS), amongst other features. Stan’s math library provides differentiable probability functions & linear algebra (C++ autodiff). Additional R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation.

Torsten: https://github.com/metrumresearchgroup/torsten

  • Torsten is a collection of Stan functions to facilitate analysis of pharmacometric data using Stan, a flexible open-source software platform for Bayesian data analysis using Hamiltonian Monte Carlo (HMC) simulation – a type of MCMC Simulation. Torsten provides Stan language functions that calculate amounts in each compartment, given an event schedule and an ODE system. The models and data format are based on NONME®/NMTRAN/PREDPP conventions. The most recent versions of Torsten support within chain parallel computation for models that require numerical solution of ODEs.

xpose: github.com/UUPharmacometrics/xpose

  • xpose was designed as a ggplot2-based update to xpose4. xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.

yspec: https://github.com/metrumresearchgroup/yspec

  • Data specifications, wrangling, and documentation.Use yspec to document analysis data sets and utilize data attributes in a modeling and simulation workflow.