nlmixr: an R package for population PKPD modeling

nlmixr

Authors: Matthew Fidler, Yuan Xiong, Rik Schoemaker, Justin Wilkins, Mirjam Trame, Teun Post, Richard Hooijmaijers, Wenping Wang

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. The nlme and SAEM estimation routines can be accessed using a universal user interface (UUI), that provides universal model and parameter defintion syntax and results in a fit object that can be used as input into the Xpose package. Running nlmixr using the UUI is described in the vignette: ihttps://github.com/nlmixrdevelopment/nlmixr/blob/master/vignettes/running_nlmixr.Rmd

Under the hood nlmixr has five main modules:

  1. dynmodel() and its mcmc cousin dynmodel.mcmc() for nonlinear dynamic models of individual data;
  2. nlme_lin_cmpt()for one to three linear compartment models of population data with first order absorption, or i.v. bolus, or i.v. infusion using the nlme algorithm;
  3. nlme_ode() for general dynamic models defined by ordinary differential equations (ODEs) of population data using the nlme algorithm;
  4. saem_fit for general dynamic models defined by ordinary differential equations (ODEs) of population data by the Stochastic Approximation Expectation-Maximization (SAEM) algorithm;
  5. gnlmm for generalized non-linear mixed-models (possibly defined by ordinary differential equations) of population data by the adaptive Gaussian quadrature algorithm.

A few utilities to facilitate population model building are also included in nlmixr.

For a brief vignette describing the modules, please see: https://github.com/nlmixrdevelopment/nlmixr/blob/master/inst/nlmixr-intro.pdf

The examples in the vignette can be run using VignetteDemo.R and the associated data files available at: https://github.com/nlmixrdevelopment/nlmixr/tree/master/vignettes

For PKPD modeling (with ODE and dosing history) with Stan, check out Yuan’s package PMXStan: https://github.com/yxiong1/pmxstan.

Using a Docker Image for running nlmixr

One of the easiest way to setup nlmixr is to docker image. For more details see:

https://github.com/nlmixrdevelopment/nlmixr/releases/download/v9.0.1-0/dockerInstall.pdf

Windows installer

For those not interested in customized installation on Windows, we recommend you download a Windows installer for your platform from the following link: https://github.com/nlmixrdevelopment/nlmixr/releases/

STOP AT THIS POINT IF YOU USED THE INSTALLER

Sometimes trying to install yourself will corrupt the installation and nlmixr will not run.

Installation on Windows

To replicate the environment that was used in Windows for nlmixr development, you will need administrator rights, and you should perform the following steps:

  1. Install R 3.5.0 (or later) from the R website.
  • For best results, we suggest you use C:\R\R-3.5.0, but you can also use the default location (C:\Program Files\R\R-3.5.0) as well, if really needed.
  • For 64-bit Windows, it is best practice to include only the 64-bit version. If you include 32-bit files, some packages may not run correctly. Additionally, both the 32- and 64-bit binaries have to be compiled for every package. Similarly, if on 32-bit Windows, install only the 32-bit version of R (and Python, and Rtools).
  1. Install the appropriate version of Rtools for Windows, currently version 3.5, from here.
  • This is an absolute requirement, since it includes C++ and related compilers not usually available under Windows.
  • For best results, use the default location of c:\Rtools
    • RxODE, a required component of nlmixr, checks and sets up the path based on the following: a. Rtools is in the path (fastest and recommended option) b. Rtools was installed with information saved to the Windows registry, and RxODE can find the installation. c. Rtools is on a hard drive installed in either Rtools or RBuildTools
    • If you are on 64-bit windows, please do not install the R 3.3.x 32-bit toolchain. These files can interfere with some packages that compile binaries, with unpredictable consequences. Similarly, only install 32-bit Rtools on 32-bit versions of Windows.
  • Make sure the compilers have been added to the Windows PATH environment variable, or RxODE and nlmixr will not work (this should be done automatically during installation).
  1. Install a version of Python for Windows.
  • This is used for its symbolic algebra package SymPy.
  • If you already have a python installed, it is much easier to piggy-back on that system than install two different python versions.
  • If you are new to python, a very robust Python distribution that includes SymPy and many packages that may be useful to the data scientist and/or pharmacometrician is Anaconda. Although very straightforward and easy to install, it is quite a large download and contains much more than you will need to run nlmixr. When installing, use the Python 3.6 version. During the installation, Anaconda provides the option of adding itself to the PATH environment variable, but advises against it; please do this anyway (despite the red warning).
  • Another option is to use official Python, although you will need to install SymPy separately if you go this route, which is sometimes not straightforward under Windows 10 owing to folder permissions (see here for a few workarounds). Nonetheless, see here for instructions for installation from source or using pip.
  • Regardless of the option you choose, please use like with like (64-bit Python for 64-bit Windows, for example).
  • Once again, make sure Python has been added to the Windows PATH environment variable, or RxODE and nlmixr will not work, no matter what Anaconda might say.
  1. Install devtools.
  • This package is required to install packages from Github, amongst other things.
  • This can be done from a clean R session by install.packages("devtools").
  1. Load devtools using library(devtools).
  2. Install RxODE.
  • Currently the new version of RxODE is in the process of being uploaded to CRAN. nlmixr needs this newer version of RxODE to function correctly. To install this version, use the command: install_github("nlmixrdevelopment/RxODE").
  • Once installed, type RxODE::rxWinPythonSetup() to install the required package SnakeCharmRand to make sure Python and SymPy are working properly. Since SnakeCharmR is orphaned, it may not work without installing directly from github with the following command: devtools::install_github("nlmixrdevelopment/SnakeCharmR")
  • Restart your R session.
  • As a quick test, you can make sure that R and Python can communicate by typing the command library(SnakeCharmR).
  • To validate or test the installation of RxODE completely, you can type the following library(RxODE); rxTest(); and it will run all of the unit tests in RxODE to make sure it is running correctly on your system. (Note that the testthat package is required for this, and it will take a long time.). This also requires installing RxODE with the tests. Devtools changed so that tests are no longer installed by default. The correct command to install the test suite is:
devtools::install_github("nlmixrdevelopment/RxODE", INSTALL_opts="--install-tests")
  1. Install nlmixr.
  • Load devtools again using library(devtools)
  • Since nlmixr shares some stan headers, nlmixr now needs C++14 support. To ease the setup you can run the command RxODE::rxC14() which creates the following build file in ~/.R/Makevars.win. If there are problems you may have to edit the file manually to match your configuration.
# Makvars.win
CXX14=$(BINPREF)g++ $(M_ARCH)
CXX14STD=-std=c++1y
CXX14FLAGS=-O2 -Wall

Installation on Linux

Instructions for Ubuntu-alike distributions are given here (specifically, Ubuntu 16.04 Xenial Xerus), but all current Linux distributions are supported, in principle.

  1. Install R 3.4.1 (or later) from an appropriate repository (Ubuntu Xenial shown below, based on instructions provided here).
  • You will need administrator privileges (i.e. access to sudo). Provide your admin password when asked.
  • Add the official CRAN repository for Ubuntu: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
  • Add the official CRAN repository for Ubuntu: sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'. If you aren’t using Ubuntu Xenial, change xenial to match your distribution’s codename.
  • Now refresh the package list using sudo apt-get update.
  • We can now install base R and required development libraries, and their dependencies: sudo apt-get install r-base r-base-dev libssl-dev
  1. Install Python dependencies.
  • Enter this: sudo apt-get install python-sympy python-pip python-setuptools python3-pip python-dev python3-dev.
  1. Install devtools and dependencies.
  • This package is required to install packages from Github, amongst other things.
  • Some Linux distributions don’t include build tools out of the box. To be safe, check this: sudo apt-get install build-essential
  • Install devtools from a clean R session by entering install.packages("devtools").
  1. In R, load devtools using library(devtools).
  2. Install RxODE.
  • Currently the new version of RxODE is in the process of being uploaded to CRAN. nlmixr needs this newer version of RxODE to function correctly. To install this version, use the command: install_github("nlmixrdevelopment/RxODE").
  • Install SnakeCharmR using install_github("nlmixrdevelopment/SnakeCharmR").
  • Restart your R session.
  • As a quick test, you can make sure that R and Python can communicate by typing the command library(SnakeCharmR).
  • To validate or test the installation of RxODE completely, you can type the following library(RxODE); rxTest(); and it will run all of the unit tests in RxODE to make sure it is running correctly on your system. (Note that the testthat package is required for this, and it will take a long time.)
  1. Install nlmixr.
  • This can be done by install_github("nlmixrdevelopment/nlmixr")

Installation on macOS

Instructions for macOS 10.13 Mojave are provided here. They should be broadly extensible to all recent releases of macOS, however.

  1. Install R 3.6.1 from the R website.
  • Download and install R-3.6.1.pkg (or later) from CRAN.
  1. Install Python dependencies.
  • Install pip from the macOS terminal prompt: sudo easy_install pip.
    • If easy_install doesn’t work, you can download by curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py and then run sudo python get-pip.py
  • Install sympy using pip: sudo -H pip install sympy.
  1. Install build tools.
  • Install the clang 8.0+ and gfortran 6.1+ packages from CRAN development tools website
  • Install Mac OSX command like tools by typing xcode-select --install and then choose install. This allows you to compile the package without downloading the entire (large) Xcode package. Make sure to perform this step even if the Xcode package is already installed.
  • If you are using macOS >= 10.14 you have to also type the following in the terminal to install the compiler headers:
sudo installer -pkg \
/Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg \
-target /
  • Change ~/.Rprofile to have the following line to adjust the paths in R to use the CRAN compilers (if not present, create the .Rprofile file):
# make sure to adjust the version number of clang based on the downloaded version in step 3 (e.g. clang8/bin)
Sys.setenv("PATH"=paste0("/usr/local/clang8/bin:/usr/local/gfortran/bin:", Sys.getenv("PATH")))

Make Sure you have an extra line at the end of this file; Otherwise R will not pick up the correct paths.

You also need to create a directory ~/.R and then put a file Makevars in it; The context of the file should contain:

# ~/.R/Makevars
CXX14FLAGS=-O2 -march=native -mtune=native -arch x86_64 -ftemplate-depth-256 -fPIC
  1. Install devtools and dependencies.
  • This package is required to install packages from Github, amongst other things.
  • Install devtools from a clean R session by entering install.packages("devtools").
  1. In R, load devtools using library(devtools).
  2. Install RxODE.
  • Currently the new version of RxODE is in the process of being uploaded to CRAN. nlmixr needs this newer version of RxODE to function correctly. To install this version, use the command: install_github("nlmixrdevelopment/RxODE").
  • Install reticulate using install.packages("reticulate").
  • Restart your R session.
  • To validate or test the installation of RxODE completely, you can type the following library(RxODE); rxTest(); and it will run all of the unit tests in RxODE to make sure it is running correctly on your system. (Note that the testthat package is required for this, and it will take a long time.)
    • Note this requires a special install option to make sure the tests are installed: remote:install_github("nlmixrdevelopment/RxODE", INSTALL_opts="--install-tests")
  1. Install nlmixr.
  • This can be done by install_github("nlmixrdevelopment/nlmixr")

Notes on Python configuration

If you have multiple versions of python installed on your system, you may run into some more issues. To be more careful in your install, you can install SnakeCharmR as follows:

Sys.setenv(SNAKECHARMR_PYTHON_VERSION="/path/to/python/with/sympy/installed")
devtools::install_github("nlmixrdevelopment/SnakeCharmR")

You may also have to adjust the following python variables to match the version with sympy installed:

Environmental Variable Correct value
PYTHON_EXE Path where the python with sympy is installed
PYTHONHOME Path where the python with sympy is installed
PYTHON_INCLUDE Path where the python libaries are installed; In windows this is PYTHONHOME\include
PYTHON_LIB Path where python libraries are installed; In windows this is PYTHONHOME\libs
PYTHONPATH Path where python searches. In windows this is a path-style variable including PYTHONHOME\DLLs, PYTHONHOME\Lib and PYTHONHOME\Lib\site-packages.
PYTHONSTARTUP In windows, this value is unset if present

I believe you could also unset some of these variables and python can figure them out. Please be warned if these variables are not setup correctly a call to python will abort python. The python abort does not respect R’s way of doing things and will also abort/crash R.

To test without running a model you may wish to try

library(SnakeCharmR)

If it doesn’t crash R, then python is likely setup correctly.

After that point you can then type:

library(RxODE)
rxSymPyVersion()

It will show the version of sympy that you are using if SnakeCharmR is setup correctly.

Testing the install

Once nlmixr is installed, you can test if everything is working well (as well as running shinyMixR) by running this install checker by Richard Hooijmaijers & Matt Fidler.

Once downloaded you would source the code; If successful you should see something similar to the following:

> source("test_install.R")
Correct R version: Yes, R version 3.5.0 (2018-04-23)
RxODE installed: Yes
Python installed: Yes, Python 3.6.2
sympy installed: Yes
devtools package installed: Yes
Rtools installed: Yes
RxODE package installed: Yes
SnakeCharmR package installed: Yes
nlmixr package installed: Yes
xpose.nlmixr package installed: Yes
shinyMixR package installed: Yes
Loading required package: nlme
nlmixr run under nlme: Yes
nlmixr run under saem: Yes
---- Installation test finished! ----