UseCase17_1 : Steady-state model

This example script is intended to illustrate how to use the 'ddmore' R package to perform a M&S workflow using the DDMoRe Standalone Execution Environment (SEE).

The following steps are implemented in this workflow:

To run a task, select with the cursor any code lines you wish to execute and press CTRL+R+R in your keyboard. An HTML file containing the commands in this file and associated output will be provided to allow the user to compare the results

Initialisation

Clear workspace and set working directory under 'UsesCasesDemo' project

rm(list=ls(all=F))
mydir <- file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),"UseCasesDemo")
setwd(mydir)

Create a working directory under 'models' folder where results are stored

uc<-"UseCase17_1"
datafile <- "warfarin_conc_SSADDL.csv"
mdlfile <- paste0(uc,".mdl")

Create a new folder to be used as working directory and where results will be stored

wd <- file.path(mydir,uc)
dir.create(wd)
## Warning in dir.create(wd):
## 'D:\SEE-Prod5_RC4\MDL_IDE\workspace\UseCasesDemo\UseCase17_1' already
## exists

Copy the dataset and the .mdl file available under “models” into the working directory

file.copy(file.path(mydir,"models", datafile),wd)
## [1] TRUE
file.copy(file.path(mydir,"models",mdlfile),wd)
## [1] TRUE

Set the working directory.

setwd(file.path(mydir,uc))

The working directory needs to be set differently when knitr R package is used to spin the file

library(knitr)
opts_knit$set(root.dir = file.path(mydir,uc)) 

List files available in working directory

list.files()
## [1] "GOF_NM.pdf"               "GOF_NM_FOCEI.pdf"        
## [3] "NONMEM"                   "UseCase17_1.mdl"         
## [5] "UseCase17_1.xml"          "UseCase17_1_FOCEI.mdl"   
## [7] "UseCase17_1_tr.txt"       "UseCase17_1_VPC.pdf"     
## [9] "warfarin_conc_SSADDL.csv"

Model Development

ESTIMATE model parameters using Monolix

The version of Monolix used in the SEE (4.3.2) does not support a combination of steady-state dosing (SS) and additional dosing records (ADDL). Therefore, this Use Case will not be converted to MLXTRAN.

SAEM Estimation with NONMEM

By default, a covariance step is not run when estimating in NONMEM. To see how it can be requested, see UseCase1_1.mdl

NM <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM")
## -- Wed Aug 17 14:56:24 2016
## New
## Submitted
## Job 8d15bb0f-a687-459f-a3ff-1e41b0debfd5 progress:
## Running [ ..................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 8d15bb0f-a687-459f-a3ff-1e41b0debfd5...
## From C:\Users\zparra\AppData\Local\Temp\RtmpSKgKTU\DDMORE.job1dec481255bc to D:/SEE-Prod5_RC4/MDL_IDE/workspace/UseCasesDemo/UseCase17_1/NONMEM
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following WARNINGs were raised during the job execution:
##  estimation_successful: 0
## 
## The following MESSAGEs were raised during the job execution:
##  covariance_step_run: 0
##  rounding_errors: 0
##  estimate_near_boundary: 0
##  s_matrix_singular: 0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Wed Aug 17 15:03:30 2016

Load previous results

NM <- LoadSOObject(“NONMEM/UseCase17_1.SO.xml”)

Results from NONMEM

parameters_nm <- getPopulationParameters(NM, what="estimates")
print(parameters_nm)
## $MLE
##     POP_CL      POP_V     POP_KA   POP_TLAG   RUV_PROP    RUV_ADD 
##  0.0976672  7.2068600  1.4571700  1.9550300  0.0000000  0.1011270 
## BETA_CL_WT  BETA_V_WT     PPV_CL  CORR_CL_V      PPV_V     PPV_KA 
##  0.7500000  1.0000000  0.1012920 -0.0193799  0.4599980  1.9930500 
##   PPV_TLAG 
##  0.1000000
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -347.9066
## 
## 
## 
## $Messages
## $Messages$Info
## $Messages$Info$covariance_step_run
## [1] "0"
## 
## $Messages$Info$rounding_errors
## [1] "0"
## 
## $Messages$Info$estimate_near_boundary
## [1] "0"
## 
## $Messages$Info$s_matrix_singular
## [1] "0"
## 
## $Messages$Info$nmoutput2so_version
## [1] "This SOBlock was created with nmoutput2so version 4.5.27"
## 
## 
## $Messages$Warnings
## $Messages$Warnings$estimation_successful
## [1] "0"

Xpose diagnostics using NONMEM output

nm.xpdb<-as.xpdb(NM,datafile)
## 
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
## 
## Residuals data does not currently contain dose rows in output from Nonmem executions.

Perform some basic goodness of fit (graphs are exported to PDF file)

print(basic.gof(nm.xpdb))

plot of chunk unnamed-chunk-9

print(ind.plots(nm.xpdb))

plot of chunk unnamed-chunk-9 plot of chunk unnamed-chunk-9

print(parm.hist(nm.xpdb))

plot of chunk unnamed-chunk-9

Export graphs to a PDF file

pdf("GOF_NM.pdf")
print(basic.gof(nm.xpdb))
print(ind.plots(nm.xpdb))
print(parm.hist(nm.xpdb))
dev.off()
## rj.GD 
##     2

Change estimation method to FOCEI (for speed)

MDL Objects can be manipulated from R to change for example the estimation algorithm

myTaskProperties <- getTaskPropertiesObjects(mdlfile)[[1]]
myNewTaskProperties <- myTaskProperties
myNewTaskProperties@ESTIMATE$algo <- "focei"

Assembling the new MOG. Note that we reuse the data and model from the previous run.

myNewerMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]], 
        parObj = getParameterObjects(mdlfile)[[1]], 
        mdlObj = getModelObjects(mdlfile)[[1]], 
        taskObj = myNewTaskProperties)

We can then write the MOG back out to an .mdl file.

mdlfile.FOCEI <- paste0(uc,"_FOCEI.mdl")
writeMogObj(myNewerMOG,mdlfile.FOCEI)

Test estimation using this new MOG.

NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Wed Aug 17 15:04:11 2016
## New
## Submitted
## Job f4b65d59-b645-4401-af31-fcb2683f6458 progress:
## Running [ .. ]
## Importing Results
## Copying the result data back to the local machine for job ID f4b65d59-b645-4401-af31-fcb2683f6458...
## From C:\Users\zparra\AppData\Local\Temp\RtmpSKgKTU\DDMORE.job1dec1ae72df1 to D:/SEE-Prod5_RC4/MDL_IDE/workspace/UseCasesDemo/UseCase17_1/NONMEM_FOCEI
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following MESSAGEs were raised during the job execution:
##  estimation_successful: 1
##  covariance_step_run: 0
##  rounding_errors: 0
##  hessian_reset: 0
##  zero_gradients: 0
##  final_zero_gradients: 0
##  estimate_near_boundary: 0
##  s_matrix_singular: 0
##  significant_digits: 3.3
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Wed Aug 17 15:04:55 2016

Load previous results

#NM.FOCEI <- LoadSOObject("NONMEM_FOCEI/UseCase17_1_FOCEI.SO.xml")

Results from NONMEM should be comparable to previous results

print(getPopulationParameters(NM.FOCEI,  what="estimates"))
## $MLE
##      POP_CL       POP_V      POP_KA    POP_TLAG    RUV_PROP     RUV_ADD 
##  0.09879320  7.15015000  0.27571300  0.68584100  0.06416220  0.10363800 
##  BETA_CL_WT   BETA_V_WT      PPV_CL   CORR_CL_V       PPV_V      PPV_KA 
##  0.75000000  1.00000000  0.08882460 -0.99982200  0.14035300  0.00449274 
##    PPV_TLAG 
##  0.10000000
print(parameters_nm)
## $MLE
##     POP_CL      POP_V     POP_KA   POP_TLAG   RUV_PROP    RUV_ADD 
##  0.0976672  7.2068600  1.4571700  1.9550300  0.0000000  0.1011270 
## BETA_CL_WT  BETA_V_WT     PPV_CL  CORR_CL_V      PPV_V     PPV_KA 
##  0.7500000  1.0000000  0.1012920 -0.0193799  0.4599980  1.9930500 
##   PPV_TLAG 
##  0.1000000

Xpose diagnostics using NONMEM output

nmfocei.xpdb<-as.xpdb(NM.FOCEI,datafile)
## 
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
## 
## Residuals data does not currently contain dose rows in output from Nonmem executions.

Basic diagnostics for NONMEM fit.

print(basic.gof(nmfocei.xpdb))

plot of chunk unnamed-chunk-16

Export graphs to a PDF file

pdf("GOF_NM_FOCEI.pdf")
print(basic.gof(nmfocei.xpdb))
dev.off()
## rj.GD 
##     2

VPC of model

Before running the VPC with PsN we must update the (initial) values in the MDL Parameter Object MLE estimates from previous step can be used

structuralPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='structural')$MLE
variabilityPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='variability')$MLE

Update the parameter object using the ddmore “updateParObj” function. This function updates an R object of (S4) class “parObj”. The user chooses which block to update, what items within that block, and what to replace those items with. NOTE: that updateParObj can only update attributes which ALREADY EXIST in the MDL Parameter Object for that item. This ensures that valid MDL is preserved.

myParObj <- getParameterObjects(mdlfile)[[1]]
myParObjUpdated <- updateParObj(myParObj,block="STRUCTURAL",
        item=names(structuralPar),
        with=list(value=structuralPar))
myParObjUpdated <- updateParObj(myParObjUpdated,block="VARIABILITY",
        item=names(variabilityPar),
        with=list(value=variabilityPar))

Check that the appropriate initial values have been updated to the MLE values from the previous fit.

print(myParObjUpdated@STRUCTURAL)
## $POP_CL
## $POP_CL$value
## [1] "0.0987932"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "7.15015"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "0.275713"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.685841"
## 
## $POP_TLAG$lo
## [1] "0.001"
## 
## 
## $BETA_CL_WT
## $BETA_CL_WT$value
## [1] "0.75"
## 
## $BETA_CL_WT$fix
## [1] "true"
## 
## 
## $BETA_V_WT
## $BETA_V_WT$value
## [1] "1"
## 
## $BETA_V_WT$fix
## [1] "true"
print(myParObjUpdated@VARIABILITY)
## $PPV_CL
## $PPV_CL$value
## [1] "0.0888246"
## 
## 
## $PPV_V
## $PPV_V$value
## [1] "0.140353"
## 
## 
## $PPV_KA
## $PPV_KA$value
## [1] "0.00449274"
## 
## 
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.1"
## 
## $PPV_TLAG$fix
## [1] "true"
## 
## 
## $CORR_CL_V
## $CORR_CL_V$value
## [1] "-0.999822"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0641622"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.103638"
## 
## $RUV_ADD$lo
## [1] "0"

Assembling the new MOG. Note that we reuse the data and model from the previous run.

myVPCMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]], 
        parObj = myParObjUpdated, 
        mdlObj = getModelObjects(mdlfile)[[1]], 
        taskObj = getTaskPropertiesObjects(mdlfile)[[1]])

We can then write the MOG back out to an .mdl file.

mdlfile.VPC <- paste0(uc,"_VPC.mdl")
writeMogObj(myVPCMOG,mdlfile.VPC)

Similarly as above, ddmore “VPC.PsN” function can be used to run a VPC using PsN as target tool

vpcFiles <- VPC.PsN(mdlfile.VPC,samples=20, seed=12345,
        vpcOptions ="-n_simulation=10 -auto_bin=10",
        subfolder="VPC", plot=TRUE) 
## Warning in normalizePath(path.expand(path), winslash, mustWork):
## path[1]="UseCase17_1_VPC.mdl": The system cannot find the file specified
## Error in execute(model, target = "PsNgeneric", addargs = vpccommand, subfolder = subfolder, : Illegal Argument: file  UseCase17_1_VPC.mdl  does not exist.

To replay the visualisation using information from the VPC SO file

pdf(paste0(uc,"_VPC.pdf"))
print(xpose.VPC(vpc.info= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_results$path),
                vpctab= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_vpctab$path),
                main="VPC warfarin"))
## Error in print(xpose.VPC(vpc.info = file.path("./VPC", vpcFiles@RawResults@DataFiles$PsN_VPC_results$path), : error in evaluating the argument 'x' in selecting a method for function 'print': Error in file.path("./VPC", vpcFiles@RawResults@DataFiles$PsN_VPC_vpctab$path) : 
##   object 'vpcFiles' not found
dev.off()
## rj.GD 
##     2

Simulation using simulx

The mlxR package has been developed to visualize and explore models that are encoded in MLXTRAN or PharmML The ddmore function as.PharmML translates an MDL file (extension .mdl) to its PharmML representation. The output file (extension .xml) is saved in the working directory.

myPharmML <- as.PharmML(mdlfile)

Use parameter values from the FOCEI estimation

parValues <- getPopulationParameters(NM.FOCEI, what="estimates")$MLE

Simulate for the typical weight of 70. Recall that logtWT = log(WT/70).

p <- c(parValues,logtWT=0)

Parameter values used in simulation

print(p)
##      POP_CL       POP_V      POP_KA    POP_TLAG    RUV_PROP     RUV_ADD 
##  0.09879320  7.15015000  0.27571300  0.68584100  0.06416220  0.10363800 
##  BETA_CL_WT   BETA_V_WT      PPV_CL   CORR_CL_V       PPV_V      PPV_KA 
##  0.75000000  1.00000000  0.08882460 -0.99982200  0.14035300  0.00449274 
##    PPV_TLAG      logtWT 
##  0.10000000  0.00000000

Simulate PK parameters for individuals

ind <- list(name = c('KA','CL','V'))

Simulate predicted (CC) and observed concentration values (Y)

f   <- list( name = c('CC'), time = seq(0,to=50,by=1))
y   <- list( name = c('Y'), time = c(0, 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 36, 48))

Simulate for a dose of 100mg given at time 0 (oral administration)

adm <- list(type = 1, time = 0, amount = 100)

Simulate 12 subjects

g <- list( size = 12, level = 'individual', treatment=adm)

Call simulx

res  <- simulx(model =myPharmML,
               parameter = p,
               group = g,
               output = list(ind,f,y))

Simulated parameter values for each individual

print(res$parameter)
##    id        KA         CL        V
## 1   1 0.2750176 0.11171017 5.883309
## 2   2 0.2743409 0.10522202 6.464947
## 3   3 0.2750484 0.10663152 6.337212
## 4   4 0.2745251 0.09540554 7.572675
## 5   5 0.2760815 0.09445836 7.706056
## 6   6 0.2744796 0.09003392 8.272071
## 7   7 0.2746429 0.11457619 5.656550
## 8   8 0.2769550 0.09923276 7.101504
## 9   9 0.2744158 0.09322463 7.842699
## 10 10 0.2742974 0.10170264 6.841684
## 11 11 0.2771707 0.10610890 6.380153
## 12 12 0.2785074 0.08949573 8.343967

Plot simulated results

plot(ggplot() + 
                geom_line(data=res$CC, aes(x=time, y=CC, colour=id)) +
                geom_point(data=res$Y, aes(x=time, y=Y, colour=id)) +
                xlab("time (h)") + ylab("concentration") )

plot of chunk unnamed-chunk-32

Simulate 1000 subjects - with simulx this is a QUICK process!

g <- list( size = 1000, level = 'individual',  treatment = adm)

Call simulx

res.1000  <- simulx(model =myPharmML,
                    parameter = p,
                    group = g,
                    output = list(ind,y))

Plot of observed concentrations (with residual error) band defines the percentile bands displayed level = range of values to examine (in %) 100 = full range of values number = number of bins within the level range.

print(prctilemlx(res.1000$Y,band=list(number=9, level=90)))

plot of chunk unnamed-chunk-34

Table of the same information

print(prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=FALSE)$y)
##    time         0%        10%         20%         30%         40%
## 1   0.0 -0.2295704 -0.1160725 -0.06513817 -0.03961506 -0.01938399
## 2   0.5 -0.3233166 -0.1637855 -0.12369232 -0.06072311 -0.02693879
## 3   1.0  0.2431164  0.7392224  0.94106240  1.05215459  1.09264818
## 4   2.0  2.6387713  3.4491070  3.66768284  3.86133056  3.99759784
## 5   3.0  4.1461970  5.3466905  5.82222993  6.05167366  6.26024680
## 6   4.0  4.8923867  6.6681346  7.38761717  7.67438323  7.90181178
## 7   6.0  7.1115159  8.4436749  9.28419828  9.55405800  9.76295716
## 8   8.0  7.4138563  9.5197570 10.07145929 10.66452338 11.25326838
## 9  12.0  8.5188843  9.9242600 10.41815974 10.97218698 11.36729374
## 10 24.0  7.9361090  9.2917645  9.58719470 10.12568276 10.43795382
## 11 36.0  7.6240228  8.1454065  8.30913780  8.73150964  8.91573237
## 12 48.0  6.0133161  6.8119309  7.00853491  7.28504014  7.37444232
##            50%         50%         60%         70%         80%        90%
## 1  -0.00747455 -0.00747455  0.01768747  0.03287448  0.06560514  0.1463758
## 2  -0.01792781 -0.01792781  0.01005285  0.03899852  0.07366971  0.1204672
## 3   1.18258123  1.18258123  1.28843163  1.39104015  1.53162652  1.7157404
## 4   4.17647120  4.17647120  4.37057221  4.50879532  4.77874329  5.1400852
## 5   6.47201539  6.47201539  6.69567855  7.02056718  7.39034939  7.8028402
## 6   8.11085115  8.11085115  8.37403454  8.72919308  9.01542654  9.5130016
## 7  10.27242820 10.27242820 10.55600999 10.84927282 11.44789871 12.5023608
## 8  11.49776590 11.49776590 11.76225718 12.24576780 12.64731805 13.3961598
## 9  11.76824359 11.76824359 12.33946926 12.65090459 13.03415462 13.8244864
## 10 10.68233001 10.68233001 11.00959632 11.29328013 11.55430298 11.8557899
## 11  9.11259645  9.11259645  9.23350886  9.52520878  9.68214138 10.0350103
## 12  7.52060096  7.52060096  7.59455261  7.77249141  7.94811313  8.3115450
##          100%
## 1   0.1959391
## 2   0.2999353
## 3   2.0520979
## 4   6.1444236
## 5   9.1507518
## 6  11.2539038
## 7  14.7368337
## 8  16.4738547
## 9  16.8025650
## 10 12.9116710
## 11 10.8381089
## 12  9.4179157