Guided-demo: Exploring 'ddmore' R functionality with the warfarin model

UseCase2 : Warfarin population pharmacokinetics model with analytical solutions

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 wiht the coursor any code lines you wish to execute and press CTRL+R+R in your keyboard.

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)

Set name of .mdl file and dataset for future tasks

uc <- "UseCase2"
datafile <- "warfarin_conc.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)

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] "UseCase2.mdl"      "warfarin_conc.csv"

Introduction to 'ddmore' R package

View objects within the .mdl file

Use 'ddmore' function getMDLObjects() to retrieve model object(s) from an existing .mdl file. This function reads the MDL in an .mdl file and parses the MDL code for each MDL Object into an R list of objects of appropriate types with names corresponding to the MDL Object names given in the file.

myMDLObj <- getMDLObjects(mdlfile)
length(myMDLObj)
## [1] 4
names(myMDLObj)
## [1] "warfarin_PK_ANALYTIC_dat" "warfarin_PK_ODE_par"     
## [3] "warfarin_PK_ANALYTIC_mdl" "warfarin_PK_ODE_task"

Use 'ddmore' function getDataObjects() to retrieve only data object(s) from an existing .mdl file This function returns a list of Parameter Object(s) from which we select the first element Hover over the variable name to see its structure

myDataObj <- getDataObjects(mdlfile)[[1]]

Use 'ddmore' function getParameterObjects() to retrieve only parameter object(s) from an existing .mdl file

myParObj <- getParameterObjects(mdlfile)[[1]]

Use 'ddmore' function getModelObjects() to retrieve only model object(s) from an existing .mdl file.

myModObj <- getModelObjects(mdlfile)[[1]]

Use 'ddmore' function getTaskPropertiesObjects() to retrieve only task properties object(s) from an existing .mdl file

myTaskObj <- getTaskPropertiesObjects(mdlfile)[[1]]

Exploratory Data Analysis

Recall that getDataObjects only reads the MDL code from the .mdl file. Use 'ddmore' function readDataObj() to create an R object from the MDL data object.

myData <- readDataObj(myDataObj)

Let's look at the first 6 lines of the data set

head(myData)
##   ID TIME   WT AMT DVID  DV MDV      logtWT
## 1  1  0.0 66.7 100    0  NA   1 -0.04829029
## 2  1  0.5 66.7  NA    1 0.0   0 -0.04829029
## 3  1  1.0 66.7  NA    1 1.9   0 -0.04829029
## 4  1  2.0 66.7  NA    1 3.3   0 -0.04829029
## 5  1  3.0 66.7  NA    1 6.6   0 -0.04829029
## 6  1  6.0 66.7  NA    1 9.1   0 -0.04829029

Extract only observation records

myEDAData<-myData[myData$MDV==0,]

Open an R window to record and access all your plots

windows(record=TRUE) 

Plot the data using xyplot from the lattice library

plot1 <- xyplot(DV~TIME,groups=ID,data=myEDAData,type="b",ylab="Conc. (mg/L)",xlab="Time (h)")
print(plot1)

plot of chunk unnamed-chunk-15

plot2 <- xyplot(DV~TIME|ID,data=myEDAData,type="b",layout=c(3,4),ylab="Conc. (mg/L)",xlab="Time (h)",scales=list(relation="free"))
print(plot2)

plot of chunk unnamed-chunk-15 plot of chunk unnamed-chunk-15 plot of chunk unnamed-chunk-15

Export the results in a pdf file

pdf(paste0(uc,"_EGA.pdf"))
 print(plot1)
 print(plot2)
dev.off()
## rj.GD 
##     2

Model Development

ESTIMATE model parameters using Monolix

The ddmore “estimate” function translates the contents of the .mdl file to a target language and then estimates parameters using the target software. After estimation, the output is converted to a Standard Output object which is saved in a .SO.xml file.

Translated files and Monolix output will be returned in the ./Monolix subfolder. The Standard Output object (.SO.xml) is read and parsed into an R object called “mlx” of (S4) class “StandardOutputObject”.

mlx <- estimate(mdlfile, target="MONOLIX", subfolder="Monolix")
## -- Fri Dec 11 02:15:46 2015
## New
## Submitted
## Job 10c107cb-181d-4c18-802b-027c766a7356 progress:
## Running [ ........ ]
## Importing Results
## Copying the result data back to the local machine for job ID 10c107cb-181d-4c18-802b-027c766a7356...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd0399a3e8f to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase2/Monolix
## Done.
## 
## 
## The following elements were parsed successfully:
##       ToolSettings
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:PrecisionPopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## Completed
## -- Fri Dec 11 02:18:31 2015
slotNames(mlx)
## [1] "ToolSettings"     "RawResults"       "TaskInformation" 
## [4] "Estimation"       "ModelDiagnostic"  "Simulation"      
## [7] "OptimalDesign"    ".pathToSourceXML"

The ddmore “LoadSOObj” function reads and parses existing Standard Output Objects mlx <- LoadSOObject(“Monolix/UseCase2.SO.xml”) The ddmore function “getPopulationParameters” extracts the Population Parameter values from an R object of (S4) class “StandardOutputObject” and returns the estimates. See documentation for getPopulationParameters to see other arguments and settings for this function.

parameters_mlx <- getPopulationParameters(mlx, what="estimates")$MLE
print(parameters_mlx)
##        POP_V    BETA_V_WT       POP_KA       POP_CL   BETA_CL_WT 
##      8.04520      1.00000      1.44422      0.13407      0.75000 
##     POP_TLAG        PPV_V       PPV_KA       PPV_CL     PPV_TLAG 
##      0.94313      0.13562      0.61255      0.26521      0.37358 
## ETA_V_ETA_CL      RUV_ADD     RUV_PROP 
##      0.16902      0.30286      0.05022
print(getPopulationParameters(mlx, what="precisions"))
## $MLE
##       Parameter     MLE      SE    RSE
## 1    BETA_CL_WT 0.75000 0.00000   0.00
## 2     BETA_V_WT 1.00000 0.00000   0.00
## 3  ETA_V_ETA_CL 0.16902 0.20027 118.49
## 4        POP_CL 0.13407 0.00642   4.79
## 5        POP_KA 1.44422 0.38306  26.52
## 6      POP_TLAG 0.94313 0.17586  18.65
## 7         POP_V 8.04520 0.21996   2.73
## 8        PPV_CL 0.26521 0.03453  13.02
## 9        PPV_KA 0.61255 0.21395  34.93
## 10     PPV_TLAG 0.37358 0.14102  37.75
## 11        PPV_V 0.13562 0.02178  16.06
## 12      RUV_ADD 0.30286 0.04784  15.80
## 13     RUV_PROP 0.05022 0.00910  18.12
print(getEstimationInfo(mlx))
## $Likelihood
## $Likelihood$LogLikelihood
## [1] -323.265
## 
## $Likelihood$IndividualContribToLL
##    Subject ICtoLL
## 1        1 -21.10
## 2        2  -5.25
## 3        3 -12.87
## 4        4 -12.25
## 5        5 -10.95
## 6        6  -7.72
## 7        7 -17.82
## 8        8 -21.72
## 9        9 -33.12
## 10      10  -5.77
## 11      12 -20.00
## 12      13 -12.10
## 13      14 -17.31
## 14      15 -12.59
## 15      16 -13.42
## 16      17  -5.71
## 17      18  -5.27
## 18      19  -6.44
## 19      20  -5.45
## 20      21  -5.91
## 21      22  -5.91
## 22      23  -8.21
## 23      24  -4.88
## 24      25  -7.09
## 25      26  -7.17
## 26      27  -5.27
## 27      28  -6.79
## 28      29  -5.63
## 29      30  -4.67
## 30      31  -5.04
## 31      32  -4.72
## 32      33  -5.09
## 
## $Likelihood$InformationCriteria
## $Likelihood$InformationCriteria$AIC
## [1] 668.53
## 
## $Likelihood$InformationCriteria$BIC
## [1] 684.66
## 
## 
## 
## $Messages
## list()

Perform model diagnostics for the base model using Xpose functions Use 'ddmore' function as.xpdb() to create an Xpose database object from the Standard Output object, regardless of target software used for estimation.

mlx.xpdb<-as.xpdb(mlx,datafile)
## 
## Removed dose rows in rawData slot of SO to enable merge with Predictions data.

We can then call Xpose functions referencing this mlx.xpdb object as the input. Perform some basic goodness of fit (graphs are exported to PDF file)

print(basic.gof(mlx.xpdb))

plot of chunk unnamed-chunk-18

print(ind.plots(mlx.xpdb))

plot of chunk unnamed-chunk-18 plot of chunk unnamed-chunk-18

Export graphs to a PDF file

pdf("GOF_MLX.pdf")
 print(basic.gof(mlx.xpdb))
 print(ind.plots(mlx.xpdb))
dev.off()
## rj.GD 
##     2

SAEM Estimation with NONMEM

NM <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM")
## -- Fri Dec 11 02:18:44 2015
## New
## Submitted
## Job 2ce53754-ca10-437b-a8ea-9d20d242b155 progress:
## Running [ ..... ]
## Importing Results
## Copying the result data back to the local machine for job ID 2ce53754-ca10-437b-a8ea-9d20d242b155...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd0205f6e3c to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase2/NONMEM
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following MESSAGEs were raised during the job execution:
##  minimization_successful: 1
##  covariance_step_run: 0
##  rounding_errors: 0
##  estimate_near_boundary: 0
##  s_matrix_singular: 0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 02:20:28 2015

Load previous results NM <- LoadSOObject(“NONMEM/UseCase2.SO.xml”)

Results from NONMEM should be comparable with results from MONOLIX

parameters_nm <- getPopulationParameters(NM, what="estimates")
print(parameters_nm)
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##  0.132856000  8.151820000  1.361500000  0.833547000  0.106436000 
##      RUV_ADD       PPV_CL ETA_CL_ETA_V        PPV_V       PPV_KA 
##  0.000100001  0.262829000  0.231478000  0.139007000  0.800165000 
##     PPV_TLAG   BETA_CL_WT    BETA_V_WT 
##  0.344029000  0.750000000  1.000000000
print(parameters_mlx)
##        POP_V    BETA_V_WT       POP_KA       POP_CL   BETA_CL_WT 
##      8.04520      1.00000      1.44422      0.13407      0.75000 
##     POP_TLAG        PPV_V       PPV_KA       PPV_CL     PPV_TLAG 
##      0.94313      0.13562      0.61255      0.26521      0.37358 
## ETA_V_ETA_CL      RUV_ADD     RUV_PROP 
##      0.16902      0.30286      0.05022

Covariance step cannot be requested in the current version of the Framework

#print(getPopulationParameters(NM, what="precisions"))

print(getEstimationInfo(NM))
## $Likelihood
## $Likelihood$Deviance
## [1] -284.6856
## 
## 
## $Messages
## $Messages$Info
## $Messages$Info$minimization_successful
## [1] "1"
## 
## $Messages$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 4.5.11"

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

print(basic.gof(nm.xpdb))

plot of chunk unnamed-chunk-22

print(ind.plots(nm.xpdb))

plot of chunk unnamed-chunk-22 plot of chunk unnamed-chunk-22

print(parm.hist(nm.xpdb))

plot of chunk unnamed-chunk-22

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 Modelling Object Group (MOG). Note that we reuse the data, parameters and model from the MOG.

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 in NONMEM via PsN

NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Fri Dec 11 02:20:54 2015
## New
## Submitted
## Job 56720b6b-e01c-4f41-9d43-9fad2fd70f7a progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 56720b6b-e01c-4f41-9d43-9fad2fd70f7a...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd066a87d98 to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase2/NONMEM_FOCEI
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following MESSAGEs were raised during the job execution:
##  minimization_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: 3.0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 02:22:19 2015

Load previous results NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase2_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 
##    0.1344280    8.0691200    1.4259600    0.9191610    0.0661158 
##      RUV_ADD       PPV_CL ETA_CL_ETA_V        PPV_V       PPV_KA 
##    0.2605030    0.2638970    0.2255100    0.1368300    0.9662940 
##     PPV_TLAG   BETA_CL_WT    BETA_V_WT 
##    0.0732954    0.7500000    1.0000000
print(parameters_nm)
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##  0.132856000  8.151820000  1.361500000  0.833547000  0.106436000 
##      RUV_ADD       PPV_CL ETA_CL_ETA_V        PPV_V       PPV_KA 
##  0.000100001  0.262829000  0.231478000  0.139007000  0.800165000 
##     PPV_TLAG   BETA_CL_WT    BETA_V_WT 
##  0.344029000  0.750000000  1.000000000

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-28

Export graphs to a PDF file

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

Run the Bootstrap using PsN

The ddmore “bootstrap.PsN” function is a wrap up function that calls Bootstrap PsN functionality using as input an MDL file that will be translated to NMTRAN as first step. Additional PsN arguments can be specified under the “bootstrapOptions” attribute. After task execution, the output from PsN is converted to a Standard Output object which is saved in a .SO.xml file. Translated files and PsN output will be returned in the ./Bootstrap subfolder

bootstrapResults <- bootstrap.PsN(mdlfile.FOCEI, samples=20, seed=123456,
        bootstrapOptions=" -no-skip_minimization_terminated -threads=2",
        subfolder="Bootstrap", plot=TRUE)
## -- Fri Dec 11 02:22:27 2015
## New
## Submitted
## Job 1b745841-1d10-42be-b824-3b6aae1b305f progress:
## Running [ ........ ]
## Importing Results
## Copying the result data back to the local machine for job ID 1b745841-1d10-42be-b824-3b6aae1b305f...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd063f9503d to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase2/Bootstrap
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Estimation:PopulationEstimates
##       Estimation:PrecisionPopulationEstimates
##       Estimation:IndividualEstimates
##       Estimation:Residuals
##       Estimation:Predictions
##       Estimation:Likelihood
## 
## The following WARNINGs were raised during the job execution:
##  bootstrap_parameter_scale: The parameters PPV_CL, ETA_CL_ETA_V, PPV_V, PPV_KA and PPV_TLAG were requested on the sd/corr scale but are given on the var/cov scale in all bootstrap results.
## 
## The following MESSAGEs were raised during the job execution:
##  minimization_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: 3.0
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 02:25:13 2015
## Warning: NAs introduced by coercion
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plot of chunk Bootstrap

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plot of chunk Bootstrap

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Load results from a bootstrap previously performed bootstrapResults <- LoadSOObject(“Bootstrap/UseCase2_FOCEI.SO.xml”) Export bootstrap histograms to a pdf

pdf(paste0(uc,"_Bootstrap.pdf"))
 print(boot.hist(results.file = file.path("Bootstrap",paste0("raw_results_",uc,"_FOCEI.csv")),
                incl.ids.file = file.path("Bootstrap","included_individuals1.csv")))
## [[1]]
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dev.off()
## rj.GD 
##     2

Extract parameter estimates and precision from bootstrap results.

print(getPopulationParameters(bootstrapResults, what="estimates"))
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##    0.1344280    8.0691200    1.4259600    0.9191610    0.0661158 
##      RUV_ADD       PPV_CL ETA_CL_ETA_V        PPV_V       PPV_KA 
##    0.2605030    0.2638970    0.2255100    0.1368300    0.9662940 
##     PPV_TLAG   BETA_CL_WT    BETA_V_WT 
##    0.0732954    0.7500000    1.0000000 
## 
## $Bootstrap
##                 Parameter        Mean     Median
## POP_CL             POP_CL 0.134977100 0.13629550
## POP_V               POP_V 8.084488000 8.02689500
## POP_KA             POP_KA 1.394247000 1.18184000
## POP_TLAG         POP_TLAG 0.831877100 0.85754450
## RUV_PROP         RUV_PROP 0.093486870 0.09161010
## RUV_ADD           RUV_ADD 0.093066800 0.06518400
## BETA_CL_WT     BETA_CL_WT 0.750000000 0.75000000
## BETA_V_WT       BETA_V_WT 1.000000000 1.00000000
## PPV_CL             PPV_CL 0.071243030 0.06853190
## ETA_CL_ETA_V ETA_CL_ETA_V 0.005422614 0.00630111
## PPV_V               PPV_V 0.015298040 0.01523810
## PPV_KA             PPV_KA 0.587199000 0.58909800
## PPV_TLAG         PPV_TLAG 0.153434300 0.12285400

Extract the information regarding the precision intervals

print(getPopulationParameters(bootstrapResults, what="intervals")$Bootstrap)
##       Parameter        Mean     Median       Perc_5    Perc_95
## 1    BETA_CL_WT 0.750000000 0.75000000  0.750000000 0.75000000
## 2     BETA_V_WT 1.000000000 1.00000000  1.000000000 1.00000000
## 3  ETA_CL_ETA_V 0.005422614 0.00630111 -0.005718610 0.01431670
## 4        POP_CL 0.134977100 0.13629550  0.123605100 0.14676260
## 5        POP_KA 1.394247000 1.18184000  0.761158300 3.38496500
## 6      POP_TLAG 0.831877100 0.85754450  0.488957200 0.99999770
## 7         POP_V 8.084488000 8.02689500  7.696565000 8.55505600
## 8        PPV_CL 0.071243030 0.06853190  0.029646000 0.12302510
## 9        PPV_KA 0.587199000 0.58909800  0.011629320 1.35812800
## 10     PPV_TLAG 0.153434300 0.12285400  0.026376440 0.53597920
## 11        PPV_V 0.015298040 0.01523810  0.007392953 0.02801069
## 12      RUV_ADD 0.093066800 0.06518400  0.001099000 0.26528550
## 13     RUV_PROP 0.093486870 0.09161010  0.059302890 0.13499960

VPC of model

When basing VPC on estimation from a target software other than NONMEM we must update the parameter values.

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

In the current version of the SO standard, we need to manually update parameter names for correlation and covariance parameters to match the SO with the MDL. This will not be needed in future releases. The SO object returned from NONMEM has parameter ETA_CL_ETA_V. This needs to be renamed to conform to model Correlation name OMEGA

variabilityNames <- names(myParObj@VARIABILITY)
names(variabilityPar)[names(variabilityPar)=="ETA_CL_ETA_V"] <- grep("OMEGA",variabilityNames,value=T)
names(variabilityPar)
## [1] "PPV_CL"   "OMEGA"    "PPV_V"    "PPV_KA"   "PPV_TLAG"

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] "0.134428"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "8.06912"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "1.42596"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.919161"
## 
## $POP_TLAG$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"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0661158"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.260503"
## 
## $RUV_ADD$lo
## [1] "1.0E-4"
print(myParObjUpdated@VARIABILITY)
## $PPV_CL
## $PPV_CL$value
## [1] "0.263897"
## 
## $PPV_CL$type
## [1] "sd"
## 
## 
## $PPV_V
## $PPV_V$value
## [1] "0.13683"
## 
## $PPV_V$type
## [1] "sd"
## 
## 
## $PPV_KA
## $PPV_KA$value
## [1] "0.966294"
## 
## $PPV_KA$type
## [1] "sd"
## 
## 
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.0732954"
## 
## $PPV_TLAG$type
## [1] "sd"
## 
## 
## $OMEGA
## $OMEGA$parameter
## [1] "[ETA_CL,ETA_V]"
## 
## $OMEGA$value
## [1] "0.22551"
## 
## $OMEGA$type
## [1] "corr"

A bug in the writeMogObj function means that for now, we must manually add the square bracket around the OMEGA value to signify that this is a vector (of length 1).

myParObjUpdated@VARIABILITY$OMEGA$value<-paste0("[",myParObjUpdated@VARIABILITY$OMEGA$value,"]")

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) 
## -- Fri Dec 11 02:25:31 2015
## New
## Submitted
## Job cdb73b0b-7976-4a74-b0e4-e04b8dbb072b progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID cdb73b0b-7976-4a74-b0e4-e04b8dbb072b...
## From C:\Users\zparra\AppData\Local\Temp\Rtmpkfi8Jd\DDMORE.jobfd071732618 to D:/SEE-1.2-SNAPSHOT-10DEC2015/MDL_IDE/workspace/UseCasesDemo/UseCase2/VPC
## Done.
## 
## 
## The following elements were parsed successfully:
##       RawResults
##       TaskInformation
##       Simulation
## 
## The following MESSAGEs were raised during the job execution:
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.11
## 
## Completed
## -- Fri Dec 11 02:26:56 2015

plot of chunk VPC

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"))
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,WT=70)

Parameter values used in simulation

print(p)
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##    0.1344280    8.0691200    1.4259600    0.9191610    0.0661158 
##      RUV_ADD       PPV_CL ETA_CL_ETA_V        PPV_V       PPV_KA 
##    0.2605030    0.2638970    0.2255100    0.1368300    0.9662940 
##     PPV_TLAG   BETA_CL_WT    BETA_V_WT           WT 
##    0.0732954    0.7500000    1.0000000   70.0000000

Simulate PK parameters for individuals

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

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

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 into the GUT (oral administration)

adm <- list(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,y))

Simulated parameter values for each individual

print(res$parameter)
##    id      TLAG        KA         CL         V
## 1   1 0.9751031 1.9734750 0.16392407  8.943269
## 2   2 0.9472053 0.7396406 0.09098205  6.590573
## 3   3 0.8748874 3.3940649 0.21153166 10.207301
## 4   4 0.9940778 2.4683725 0.16226765  8.896298
## 5   5 0.9420058 5.8249249 0.12985450  7.925592
## 6   6 0.9603786 1.4769072 0.19048453  9.667433
## 7   7 0.9574111 1.3119880 0.13865156  8.199591
## 8   8 0.9235718 2.6980526 0.14103527  8.272383
## 9   9 0.9003508 6.0186378 0.14080282  8.265311
## 10 10 0.9215054 1.5459374 0.18442151  9.506644
## 11 11 0.9584692 1.1591302 0.16743251  9.042010
## 12 12 0.9219627 3.7797648 0.15194613  8.598253

Plot simulated results

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

plot of chunk unnamed-chunk-49

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-51

Table of the same information

print(prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=F)$y)
##    time         0%         10%        20%         30%         40%
## 1   0.0 -0.7391255 -0.42493880 -0.2274938 -0.15530472 -0.09239917
## 2   0.5 -0.4245951 -0.25460315 -0.1364019 -0.06644699 -0.02280089
## 3   1.0 -0.2909170  0.07904086  0.3131799  0.75960591  1.05097461
## 4   2.0  1.9850645  4.76952879  6.6798233  7.91713915  8.74393809
## 5   3.0  3.5017489  7.47145911  8.6523156  9.70043992 10.55461290
## 6   4.0  4.6096900  8.38484527  9.7027257 10.22901903 10.73005463
## 7   6.0  5.5801819  9.28066925  9.8846360 10.39846522 11.22665551
## 8   8.0  7.1107645  9.36189802  9.9572501 10.49884088 10.76994586
## 9  12.0  6.8628038  8.39776997  9.3018386  9.84830106 10.08979624
## 10 24.0  4.7345870  7.11389481  7.8159522  8.25710739  8.62659414
## 11 36.0  2.9232873  5.42297845  5.9157595  6.33828101  6.69750631
## 12 48.0  3.3263595  4.55799880  5.0634908  5.49737983  5.80641319
##            50%         50%         60%         70%        80%        90%
## 1  -0.05318938 -0.05318938  0.01118815  0.04829482  0.1332348  0.2320606
## 2   0.05426018  0.05426018  0.11595701  0.19039761  0.2444507  0.3388918
## 3   1.23947312  1.23947312  1.65954858  2.28284386  2.9859906  3.9973121
## 4   9.56636095  9.56636095 10.50431159 11.24964930 12.0245586 13.1896802
## 5  11.15104710 11.15104710 11.88754637 12.29325490 12.7972446 13.8561111
## 6  11.28493899 11.28493899 11.80163673 12.47542456 13.1937290 14.2499205
## 7  11.74294886 11.74294886 12.40956336 12.94262926 13.4271257 13.8857945
## 8  11.24948430 11.24948430 11.86558807 12.12018439 12.9077470 13.5850201
## 9  10.57882098 10.57882098 11.14579846 11.57652149 12.3339792 13.1873846
## 10  8.83674658  8.83674658  9.21135211  9.55280681  9.9906827 11.2945898
## 11  7.09239680  7.09239680  7.36779448  7.91896885  8.6272688  9.2968454
## 12  6.10485027  6.10485027  6.50306994  6.80907164  7.4668327  8.1443363
##          100%
## 1   0.7510420
## 2   0.6877251
## 3  12.8825932
## 4  17.9225343
## 5  17.0522841
## 6  19.0081558
## 7  17.1672515
## 8  15.5657483
## 9  15.3464507
## 10 14.0674412
## 11 12.0968484
## 12 10.0777345