The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The goal of monolix2rx is to convert Monolix
to
rxode2
to use for simulation and sharing the model in an
open-source framework.
You can install the development version of monolix2rx from GitHub with:
# install.packages("devtools")
::install_github("nlmixr2/monolix2rx") devtools
If you are trying to convert a Monolix to a rxode2 model you simply
need the path to the mlxtran
file. For example, the classic
demo of theophylline is included in monolix2rx
and can be
imported below:
library(monolix2rx)
# First load in the model; in this case the theo model
# This is modified from the Monolix demos by saving the model
# file as a text file (hence you can access without model library).
# Additionally some of the file paths were shortened so they could
# be included with monolix2rx
<- system.file("theo", package="monolix2rx")
pkgTheo <- file.path(pkgTheo, "theophylline_project.mlxtran")
mlxtranFile
<- monolix2rx(mlxtranFile)
rx #> ℹ updating model values to final parameter estimates
#> ℹ done
#> ℹ reading run info (# obs, doses, Monolix Version, etc) from summary.txt
#> ℹ done
#> ℹ reading covariance from FisherInformation/covarianceEstimatesLin.txt
#> ℹ done
#> ℹ imported monolix and translated to rxode2 compatible data ($monolixData)
#> ℹ imported monolix ETAS (_SAEM) imported to rxode2 compatible data ($etaData)
#> ℹ imported monolix pred/ipred data to compare ($predIpredData)
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> ℹ solving ipred problem
#> ℹ done
#> ℹ solving pred problem
#> ℹ done
rx#> ── rxode2-based free-form 2-cmt ODE model ──────────────────────────────────────
#> ── Initalization: ──
#> Fixed Effects ($theta):
#> ka_pop V_pop Cl_pop a b
#> 0.42699448 -0.78635157 -3.21457598 0.43327956 0.05425953
#>
#> Omega ($omega):
#> omega_ka omega_V omega_Cl
#> omega_ka 0.4503145 0.00000000 0.00000000
#> omega_V 0.0000000 0.01594701 0.00000000
#> omega_Cl 0.0000000 0.00000000 0.07323701
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 depot
#> 2 2 central
#> ── μ-referencing ($muRefTable): ──
#> theta eta level
#> 1 ka_pop omega_ka id
#> 2 V_pop omega_V id
#> 3 Cl_pop omega_Cl id
#>
#> ── Model (Normalized Syntax): ──
#> function() {
#> description <- "The administration is extravascular with a first order absorption (rate constant ka).\nThe PK model has one compartment (volume V) and a linear elimination (clearance Cl).\nThis has been modified so that it will run without the model library"
#> dfObs <- 120
#> dfSub <- 12
#> thetaMat <- lotri({
#> ka_pop + V_pop + Cl_pop ~ c(0.09785, 0.00082606, 0.00041937,
#> -4.2833e-05, -6.7957e-06, 1.1318e-05)
#> a + b ~ c(0.015333, -0.0026458, 0.00056232)
#> })
#> validation <- c("ipred relative difference compared to Monolix ipred: 0.04%; 95% percentile: (0%,0.52%); rtol=0.00038",
#> "ipred absolute difference compared to Monolix ipred: 95% percentile: (0.000362, 0.00848); atol=0.00254",
#> "pred relative difference compared to Monolix pred: 0%; 95% percentile: (0%,0%); rtol=6.6e-07",
#> "pred absolute difference compared to Monolix pred: 95% percentile: (1.6e-07, 1.27e-05); atol=3.66e-06",
#> "iwres relative difference compared to Monolix iwres: 0%; 95% percentile: (0.06%,32.22%); rtol=0.0153",
#> "iwres absolute difference compared to Monolix pred: 95% percentile: (0.000403, 0.0138); atol=0.00305")
#> ini({
#> ka_pop <- 0.426994483535611
#> V_pop <- -0.786351566327091
#> Cl_pop <- -3.21457597916301
#> a <- c(0, 0.433279557549051)
#> b <- c(0, 0.0542595276206251)
#> omega_ka ~ 0.450314511978718
#> omega_V ~ 0.0159470121255372
#> omega_Cl ~ 0.0732370098834837
#> })
#> model({
#> cmt(depot)
#> cmt(central)
#> ka <- exp(ka_pop + omega_ka)
#> V <- exp(V_pop + omega_V)
#> Cl <- exp(Cl_pop + omega_Cl)
#> d/dt(depot) <- -ka * depot
#> d/dt(central) <- +ka * depot - Cl/V * central
#> Cc <- central/V
#> CONC <- Cc
#> CONC ~ add(a) + prop(b) + combined1()
#> })
#> }
# If you are only interseted in the parsing you can use `mlxtran`
<- mlxtran(mlxtranFile)
mlx #> ℹ reading run info (# obs, doses, Monolix Version, etc) from summary.txt
#> ℹ done
#> ℹ reading covariance from FisherInformation/covarianceEstimatesLin.txt
#> ℹ done
mlx#> DESCRIPTION:
#> The administration is extravascular with a first order absorption (rate constant ka).
#> The PK model has one compartment (volume V) and a linear elimination (clearance Cl).
#> This has been modified so that it will run without the model library
#>
#> <DATAFILE>
#> [FILEINFO]
#> ; parsed: $DATAFILE$FILEINFO$FILEINFO
#> file = 'data/theophylline_data.txt'
#> delimiter = tab
#> header = {ID, AMT, TIME, CONC, WEIGHT, SEX}
#>
#> [CONTENT]
#> ; parsed: $DATAFILE$CONTENT$CONTENT
#> ID = {use=identifier}
#> TIME = {use=time}
#> AMT = {use=amount}
#> CONC = {use=observation, name=CONC, type=continuous}
#> WEIGHT = {use=covariate, type=continuous}
#> SEX = {use=covariate, type=categorical}
#>
#> <MODEL>
#> [INDIVIDUAL]
#> ; parsed: $MODEL$INDIVIDUAL$INDIVIDUAL
#> input = {ka_pop, omega_ka, V_pop, omega_V, Cl_pop, omega_Cl}
#>
#> DEFINITION:
#> ; parsed: $MODEL$INDIVIDUAL$DEFINITION
#> ka = {distribution=lognormal, typical=ka_pop, sd=omega_ka}
#> V = {distribution=lognormal, typical=V_pop, sd=omega_V}
#> Cl = {distribution=lognormal, typical=Cl_pop, sd=omega_Cl}
#>
#> [LONGITUDINAL]
#> ; parsed: $MODEL$LONGITUDINAL$LONGITUDINAL
#> input = {a, b, ka, V, Cl}
#> file = 'oral1_1cpt_kaVCl.txt'
#>
#> DEFINITION:
#> ; parsed: $MODEL$LONGITUDINAL$DEFINITION
#> CONC = {distribution=normal, prediction=Cc, errorModel=combined1(a, b)}
#>
#> EQUATION:
#>
#> ; PK model definition
#> Cc = pkmodel(ka, V, Cl)
#>
#> OUTPUT:
#> ; parsed: $MODEL$LONGITUDINAL$OUTPUT
#> output = Cc
#>
#> <FIT>
#> ; parsed: $FIT$FIT
#> data = {CONC}
#> model = {CONC}
#>
#> <PARAMETER>
#> ; parsed: $PARAMETER$PARAMETER
#> Cl_pop = {value=0.1, method=MLE}
#> V_pop = {value=0.5, method=MLE}
#> a = {value=1, method=MLE}
#> b = {value=0.3, method=MLE}
#> ka_pop = {value=1, method=MLE}
#> omega_Cl = {value=1, method=MLE}
#> omega_V = {value=1, method=MLE}
#> omega_ka = {value=1, method=MLE}
#>
#> <MONOLIX>
#> [TASKS]
#> ; parsed: $MONOLIX$TASKS$TASKS
#> populationParameters()
#> individualParameters(method = {conditionalMean, conditionalMode})
#> fim(method = Linearization)
#> logLikelihood(method = Linearization)
#> plotResult(method = {indfits, obspred, vpc, residualsscatter, residualsdistribution, parameterdistribution, covariatemodeldiagnosis, randomeffects, covariancemodeldiagnosis, saemresults})
#>
#> [SETTINGS]
#> GLOBAL:
#> ; parsed: $MONOLIX$SETTINGS$GLOBAL
#> exportpath = 'tp'
#>
#> ; unparsed sections:
#> ; $MODEL$LONGITUDINAL$EQUATION
# this can be converted to a list
<- as.list(mlx)
mlx
$DATAFILE$FILEINFO$FILEINFO
mlx#> $file
#> [1] "data/theophylline_data.txt"
#>
#> $header
#> [1] "ID" "AMT" "TIME" "CONC" "WEIGHT" "SEX"
#>
#> $delimiter
#> [1] "tab"
For models using Monolix’s model library, the models may not be
accessible as text files in all versions of Monolix. In the
mlxtran
files you may see something like:
lib:bolus_1cpt_TlagVCl.txt
For older versions of Monolix, the model libraries are a group of
text files. You can find it by looking for a file in the Monolix library
like bolus_1cpt_TlagVCl.txt
. In this case it would be in
pk/bolus_1cpt_TlagVCl.txt
. The parent directory would be
the model library. If you have access to these files (even if they are
from an old version of Monolix) you can make monolix2rx
aware of the model library by using:
# If the model library was located in ~/src/monolix/library
# Then you would set the model library up as follows:
options(monolix2rx.library="~/src/monolix/library/")
In Unix, this can be a symbolic link to whatever model library you would like to use.
You can check to see if it works by trying to translate the model
file to rxode2
:
monolix2rx("lib:bolus_1cpt_TlagVCl.txt")
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> ℹ cannot find individual parameter estimates
#> ── rxode2-based free-form 1-cmt ODE model ──────────────────────────────────────
#>
#> States ($state or $stateDf):
#> Compartment Number Compartment Name
#> 1 1 central
#> ── Model (Normalized Syntax): ──
#> function() {
#> description <- "The administration is via a bolus with a lag time (Tlag).\nThe PK model has one compartment (volume V) and a linear elimination (clearance Cl)."
#> model({
#> cmt(central)
#> d/dt(central) <- -Cl/V * central
#> alag(central) <- Tlag
#> Cc <- central/V
#> })
#> }
If you computer is setup correctly (like above) you will see the
translated model. Note since it isn’t a mlxtran
file the
relationship between population parameters, between subject variability
etc and initial parameter estimates are not in the model.
If the model library is not setup correctly you will see or cannot be found in an old model library you get:
try(monolix2rx("lib:notThere.txt"))
#> Warning in .mlxtranLib(file): while options('monolix2rx.library') is set, could not find model file 'lib:notThere.txt'
#> please save the model to translate
#> Error : could not find the model file
In newer versions of Monolix, the model library was turned into a
binary database that is accessed by the GUI and
lixoftConnectors
. If you have lixoftConnectors
on your system and it can successfully load the model with
lixoftConnectors::getLibraryModelContent()
then
monolix2rx
will also load the model correctly (and will use
this version over the text files when both are setup)
This means you will need to import models into rxode2
you need to:
For a model built from the model library you will need:
have a path to the text file Monolix Library and setup the
monolix2rx.library
with
options(monolix2rx.library="~/src/monolix/library/")
have lixoftConnectors
installed and connected to a
newer (and licensed) version of Monolix that can get the model library
content by
lixoftConnectors::getLibraryModelContent()
or without these options, you will need to save the model to a text file outside of the model library so you can import the model.
The tests in this package include testing the Monolix
demo files, the Monolix
library files (if available), and
Monolix validation suite.
Since these are a part of Monolix itself, they are not included in
this package. You can setup monolix2rx
to run tests on all
of these files as well by setting up some options:
# setup monolix library (and will test that the parsing and translation are as expected)
options(monolix2rx.library="~/src/monolix/library/")
# setup monolix demos to be tested
options(monolix2rx.demo="~/src/monolix/demos/")
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.