nonmem2rx

The goal of nonmem2rx is to convert a NONMEM control stream to a rxode2 (or even a nlmixr2 fit) for easy clinical trial simulation in R.

Installation

You can install the development version of nonmem2rx from GitHub with the r-universe:

install.packages('nonmem2rx', repos = c('https://nlmixr2.r-universe.dev', 'https://cloud.r-project.org'))

When on CRAN, you can also get the CRAN version by:

install.packages('nonmem2rx')

What you can do with nonmem2rx/babelmixr2

You can do many useful tasks directly converting between nlmixr2 and NONMEM models; you can:

• Convert a NONMEM model to a rxode2 model

• Do development in nlmixr2 and then run NONMEM from a nlmixr2 model for reviewers who want to know about NONMEM results.

• In both conversions, automatically make sure the model is translated correctly (for babelmixr2)

Then with nlmixr2 fit models and nonmem2rx models coming from both conversions, you can:

• Perform simulations of new dosing from the NONMEM model or even simulate using the uncertainty in your model to simulate new scenarios

• Modify the model to calculate derived parameters (like AUC). These parameters slow down NONMEM’s optimization, but can help in your simulation scenario.

• Simulating with Covariates/Input PK parameters. This example shows approaches to resample from the input dataset for covariate selection.

With nonmem2rx and babelmixr2, convert the imported rxode2 model to a nlmixr2 object, allowing:

• Generation of Word and PowerPoint plots with nlmixr2rpt

• Easy VPC creation (with vpcPlot())

• Easy Individual plots with extra solved points. This will show the curvature of individual and population fits for sparse data-sets (with augPred())

You can even use this conversion to help debug your NONMEM model (or even try it in nlmixr2 instead)

• Understand how to simplify the NONMEM model to avoid rounding errors

• Run nlmixr2’s covariance step when NONMEMs covariance step has failed (in the linked example, there was no covariance step because rounding errors)

Simple example

Once nonmem2rx has been loaded, you simply type the location of the nonmem control stream for the parser to start. For example:

library(nonmem2rx)

# First we need the location of the nonmem control stream Since we are
# running an example, we will use one of the built-in examples in
# `nonmem2rx`
ctlFile <- system.file("mods/cpt/runODE032.ctl", package="nonmem2rx")
# You can use a control stream or other file. With the development
# version of `babelmixr2`, you can simply point to the listing file
mod <- nonmem2rx(ctlFile, lst=".res", save=FALSE)
#> ℹ getting information from  '/tmp/RtmphCfr0E/temp_libpathaf275a605b00/nonmem2rx/mods/cpt/runODE032.ctl'
#> ℹ reading in xml file
#> ℹ done
#> ℹ reading in phi file
#> ℹ done
#> ℹ reading in lst file
#> ℹ abbreviated list parsing
#> ℹ done
#> ℹ done
#> ℹ splitting control stream by records
#> ℹ done
#> ℹ Processing record $INPUT
#> ℹ Processing record $MODEL
#> ℹ Processing record $THETA
#> ℹ Processing record $OMEGA
#> ℹ Processing record $SIGMA
#> ℹ Processing record $PROBLEM
#> ℹ Processing record $DATA
#> ℹ Processing record $SUBROUTINES
#> ℹ Processing record $PK
#> ℹ Processing record $DES
#> ℹ Processing record $ERROR
#> ℹ Processing record $ESTIMATION
#> ℹ Ignore record $ESTIMATION
#> ℹ Processing record $COVARIANCE
#> ℹ Ignore record $COVARIANCE
#> ℹ Processing record $TABLE
#> ℹ change initial estimate of `theta1` to `1.37034036528946`
#> ℹ change initial estimate of `theta2` to `4.19814911033061`
#> ℹ change initial estimate of `theta3` to `1.38003493562413`
#> ℹ change initial estimate of `theta4` to `3.87657341967489`
#> ℹ change initial estimate of `theta5` to `0.196446108190896`
#> ℹ change initial estimate of `eta1` to `0.101251418415006`
#> ℹ change initial estimate of `eta2` to `0.0993872449483344`
#> ℹ change initial estimate of `eta3` to `0.101302674763154`
#> ℹ change initial estimate of `eta4` to `0.0730497519364148`
#> ℹ read in nonmem input data (for model validation): /tmp/RtmphCfr0E/temp_libpathaf275a605b00/nonmem2rx/mods/cpt/Bolus_2CPT.csv
#> ℹ ignoring lines that begin with a letter (IGNORE=@)'
#> ℹ applying names specified by $INPUT
#> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),]
#> ℹ done
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0’
#> In file included from /usr/share/R/include/R.h:71,
#>                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2/include/rxode2.h:9,
#>                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
#>                  from rx_d16f021bc9a6b4f5e2be95cdc7bf3d57_.c:115:
#> /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
#>    80 |     };
#>       |      ^
#> ℹ read in nonmem IPRED data (for model validation): /tmp/RtmphCfr0E/temp_libpathaf275a605b00/nonmem2rx/mods/cpt/runODE032.csv
#> ℹ done
#> ℹ changing most variables to lower case
#> ℹ done
#> ℹ replace theta names
#> ℹ done
#> ℹ replace eta names
#> ℹ done (no labels)
#> ℹ renaming compartments
#> ℹ done
#> using C compiler: ‘gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0’
#> In file included from /usr/share/R/include/R.h:71,
#>                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2/include/rxode2.h:9,
#>                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
#>                  from rx_edd6c2bb8fc0df18bd2c37d123e584da_.c:115:
#> /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
#>    80 |     };
#>       |      ^
#> ℹ solving ipred problem
#> ℹ done
#> ℹ solving pred problem
#> ℹ done

mod
#>  ── rxode2-based free-form 2-cmt ODE model ────────────────────────────────────── 
#>  ── Initalization: ──  
#> Fixed Effects ($theta): 
#>    theta1    theta2    theta3    theta4       RSV 
#> 1.3703404 4.1981491 1.3800349 3.8765734 0.1964461 
#> 
#> Omega ($omega): 
#>           eta1       eta2      eta3       eta4
#> eta1 0.1012514 0.00000000 0.0000000 0.00000000
#> eta2 0.0000000 0.09938724 0.0000000 0.00000000
#> eta3 0.0000000 0.00000000 0.1013027 0.00000000
#> eta4 0.0000000 0.00000000 0.0000000 0.07304975
#> 
#> States ($state or $stateDf): 
#>   Compartment Number Compartment Name
#> 1                  1          CENTRAL
#> 2                  2             PERI
#>  ── μ-referencing ($muRefTable): ──  
#>    theta  eta level
#> 1 theta1 eta1    id
#> 2 theta2 eta2    id
#> 3 theta3 eta3    id
#> 4 theta4 eta4    id
#> 
#>  ── Model (Normalized Syntax): ── 
#> function() {
#>     description <- "BOLUS_2CPT_CLV1QV2 SINGLE DOSE FOCEI (120 Ind/2280 Obs) runODE032"
#>     validation <- c("IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06", 
#>         "IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.19e-05, 0.0418); atol=0.00167", 
#>         "IWRES relative difference compared to Nonmem IWRES: 0%; 95% percentile: (0%,0.01%); rtol=8.99e-06", 
#>         "IWRES absolute difference compared to Nonmem IWRES: 95% percentile: (1.82e-07, 4.63e-05); atol=3.65e-06", 
#>         "PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06", 
#>         "PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382) atol=6.41e-06")
#>     ini({
#>         theta1 <- 1.37034036528946
#>         label("log Cl")
#>         theta2 <- 4.19814911033061
#>         label("log Vc")
#>         theta3 <- 1.38003493562413
#>         label("log Q")
#>         theta4 <- 3.87657341967489
#>         label("log Vp")
#>         RSV <- c(0, 0.196446108190896, 1)
#>         label("RSV")
#>         eta1 ~ 0.101251418415006
#>         eta2 ~ 0.0993872449483344
#>         eta3 ~ 0.101302674763154
#>         eta4 ~ 0.0730497519364148
#>     })
#>     model({
#>         cmt(CENTRAL)
#>         cmt(PERI)
#>         cl <- exp(theta1 + eta1)
#>         v <- exp(theta2 + eta2)
#>         q <- exp(theta3 + eta3)
#>         v2 <- exp(theta4 + eta4)
#>         v1 <- v
#>         scale1 <- v
#>         k21 <- q/v2
#>         k12 <- q/v
#>         d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1
#>         d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL
#>         f <- CENTRAL/scale1
#>         ipred <- f
#>         rescv <- RSV
#>         ipred ~ prop(RSV)
#>     })
#> }
#>  ── nonmem2rx translation notes ($notes): ──  
#>    • there are duplicate eta names, not renaming duplicate parameters 
#>    • there are duplicate theta names, not renaming duplicate parameters 
#>  ── nonmem2rx extra properties: ──  
#> other properties include: $nonmemData, $etaData, $thetaMat, $dfSub, $dfObs
#> captured NONMEM table outputs: $predData, $ipredData
#> NONMEM/rxode2 comparison data: $iwresCompare, $predCompare, $ipredCompare
#> NONMEM/rxode2 composite comparison: $predAtol, $predRtol, $ipredAtol, $ipredRtol, $iwresAtol, $iwresRtol

You can see this automatically validates NONMEM and rxode2 outputs for a couple of metrics.

External projects that contributed to the tool’s validation

The nonmem2rx tool was validated against:

• The PsN library test suite of NONMEM listings (https://github.com/UUPharmacometrics/PsN/tree/master/test)

• The ddmore model scrapings (https://github.com/dpastoor/ddmore_scraping).

• Models from NONMEM design tutorial Bauer 2021 https://doi.org/10.1002/psp4.12713

• Models from NONMEM tutorial 1 (Bauer 2019) https://doi.org/10.1002/psp4.12404

• Models from NONMEM tutorial 2 (Bauer 2019) https://doi.org/10.1002/psp4.12422

Due to the sheer size of the zipped models for these nonmem control stream sources, these are excluded to keep the binary below 3 mgs (CRAN requirement).

However, I would like to acknowledge all who helped in these projects. With these projects the NONMEM conversion to rxode2 has been made much more robust.

Still, while the tests are not/will not be in the CRAN binaries, you can test them yourself by:

1. Downloading this repository
2. Running the tests devtools::test()