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

UseCase7 : Use of PK macros to define warfarin population pharmacokinetics

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=FALSE))
mydir <- file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),"UseCasesDemo")
setwd(mydir)

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

uc<-"UseCase7"
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] "UseCase7.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_dat"              "warfarin_PK_Compartments_par"
## [3] "warfarin_PK_Compartments_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()
## png 
##   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 Standardised Output object which is saved in a .SO.xml file.

Translated files and Monolix output will be returned in the ./Monolix subfolder. The Standardised 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")
## -- Tue Aug 16 13:03:16 2016
## New
## Submitted
## Job c1333b22-a68a-494d-a83d-5fc05afe5520 progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID c1333b22-a68a-494d-a83d-5fc05afe5520...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35645cad18c8 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase7/Monolix
## Done.
## 
## 
## The following main elements were parsed successfully:
##   ToolSettings
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::PrecisionPopulationEstimates::MLE
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::IndividualContribToLL
##   Estimation::OFMeasures::InformationCriteria
##   Estimation::OFMeasures::LogLikelihood
## 
## Completed
## -- Tue Aug 16 13:04:38 2016
slotNames(mlx)
## [1] "ToolSettings"     "RawResults"       "TaskInformation" 
## [4] "Estimation"       "ModelDiagnostic"  "Simulation"      
## [7] "OptimalDesign"    ".pathToSourceXML"

The ddmore “LoadSOObj” reads and parsed existing Standardise Output Objects

mlx <- LoadSOObject(“Monolix/UseCase7.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
parameters_mlx
##      POP_V  BETA_V_WT     POP_KA     POP_CL BETA_CL_WT     POP_F1 
##    8.10773    1.00000    2.47110    0.13382    0.75000    1.00000 
##   POP_TLAG      PPV_V     PPV_KA     PPV_CL   PPV_TLAG  CORR_CL_V 
##    1.00232    0.13771    1.38800    0.26414    0.10000    0.24097 
##    RUV_ADD   RUV_PROP 
##    0.21480    0.06816
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   CORR_CL_V 0.24097 0.19588 81.29
## 4      POP_CL 0.13382 0.00637  4.76
## 5      POP_F1 1.00000 0.00000  0.00
## 6      POP_KA 2.47110 1.33897 54.18
## 7    POP_TLAG 1.00232 0.05617  5.60
## 8       POP_V 8.10773 0.22635  2.79
## 9      PPV_CL 0.26414 0.03429 12.98
## 10     PPV_KA 1.38800 0.38918 28.04
## 11   PPV_TLAG 0.10000 0.00000  0.00
## 12      PPV_V 0.13771 0.02246 16.31
## 13    RUV_ADD 0.21480 0.04371 20.35
## 14   RUV_PROP 0.06816 0.00916 13.44
getEstimationInfo(mlx)
## $OFMeasures
## $OFMeasures$LogLikelihood
## $OFMeasures$LogLikelihood[[1]]
## [1] -334.635
## 
## 
## $OFMeasures$IndividualContribToLL
##    Subject ICtoLL
## 1        1 -25.73
## 2        2  -5.25
## 3        3 -13.35
## 4        4 -12.42
## 5        5 -11.11
## 6        6  -7.29
## 7        7 -18.95
## 8        8 -20.86
## 9        9 -31.76
## 10      10  -5.77
## 11      12 -19.75
## 12      13 -18.63
## 13      14 -18.82
## 14      15 -11.58
## 15      16 -14.49
## 16      17  -5.73
## 17      18  -5.04
## 18      19  -6.69
## 19      20  -5.05
## 20      21  -5.97
## 21      22  -5.79
## 22      23  -8.07
## 23      24  -4.89
## 24      25  -7.20
## 25      26  -7.21
## 26      27  -5.35
## 27      28  -6.83
## 28      29  -5.62
## 29      30  -4.55
## 30      31  -5.04
## 31      32  -4.74
## 32      33  -5.10
## 
## $OFMeasures$InformationCriteria
## $OFMeasures$InformationCriteria$AIC
## [1] 689.27
## 
## $OFMeasures$InformationCriteria$BIC
## [1] 703.93
## 
## 
## 
## $Messages
## list()

Perform model diagnostics for the base model using Xpose functions (graphs are exported to PDF)

Use 'ddmore' function as.xpdb() to create an Xpose database object from the Standardised 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)

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

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")
## -- Tue Aug 16 13:04:40 2016
## New
## Submitted
## Job a9a33dff-e8ca-4249-9f5b-8ad3aaed082e progress:
## Running [ ..... ]
## Importing Results
## Copying the result data back to the local machine for job ID a9a33dff-e8ca-4249-9f5b-8ad3aaed082e...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35646b07249 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase7/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 MESSAGEs were raised during the job execution:
##  estimation_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 4.5.27
## 
## Completed
## -- Tue Aug 16 13:06:23 2016

The ddmore “LoadSOObj” reads and parsed existing Standardise Output Objects

NM <- LoadSOObject(“NONMEM/UseCase7.SO.xml”)

parameters_nm <- getPopulationParameters(NM,what="estimates")$MLE

print(parameters_nm)
##      POP_CL       POP_V      POP_KA    POP_TLAG    RUV_PROP     RUV_ADD 
## 0.132954000 8.173140000 1.749320000 0.945971000 0.108251000 0.000100001 
##      POP_F1  BETA_CL_WT   BETA_V_WT      PPV_CL   CORR_CL_V       PPV_V 
## 1.000000000 0.750000000 1.000000000 0.263455000 0.277717000 0.136488000 
##      PPV_KA    PPV_TLAG 
## 0.947330000 0.100000000
print(parameters_mlx)
##      POP_V  BETA_V_WT     POP_KA     POP_CL BETA_CL_WT     POP_F1 
##    8.10773    1.00000    2.47110    0.13382    0.75000    1.00000 
##   POP_TLAG      PPV_V     PPV_KA     PPV_CL   PPV_TLAG  CORR_CL_V 
##    1.00232    0.13771    1.38800    0.26414    0.10000    0.24097 
##    RUV_ADD   RUV_PROP 
##    0.21480    0.06816
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -310.3207
## 
## 
## 
## $Messages
## $Messages$Info
## $Messages$Info$estimation_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 4.5.27"

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-22

print(ind.plots(nm.xpdb))

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

print(parm.hist(nm.xpdb))

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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()
## png 
##   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.

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

NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Tue Aug 16 13:06:41 2016
## New
## Submitted
## Job ba4e4ab6-b24e-4f56-bc41-7eea90f8feff progress:
## Running [ .. ]
## Importing Results
## Copying the result data back to the local machine for job ID ba4e4ab6-b24e-4f56-bc41-7eea90f8feff...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35645f537153 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase7/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: 4.2
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 13:07:23 2016

Load previous results

NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase7_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 
##  0.1341360  8.1009100  1.5617100  0.9677450  0.0716038  0.1932260 
##     POP_F1 BETA_CL_WT  BETA_V_WT     PPV_CL  CORR_CL_V      PPV_V 
##  1.0000000  0.7500000  1.0000000  0.2637860  0.2425380  0.1348310 
##     PPV_KA   PPV_TLAG 
##  0.9360370  0.1000000
print(parameters_nm)
##      POP_CL       POP_V      POP_KA    POP_TLAG    RUV_PROP     RUV_ADD 
## 0.132954000 8.173140000 1.749320000 0.945971000 0.108251000 0.000100001 
##      POP_F1  BETA_CL_WT   BETA_V_WT      PPV_CL   CORR_CL_V       PPV_V 
## 1.000000000 0.750000000 1.000000000 0.263455000 0.277717000 0.136488000 
##      PPV_KA    PPV_TLAG 
## 0.947330000 0.100000000

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()
## png 
##   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 Standardised 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)
## -- Tue Aug 16 13:07:27 2016
## New
## Submitted
## Job 271b67ac-89fc-4d7b-921f-d3544a2686cf progress:
## Running [ ....... ]
## Importing Results
## Copying the result data back to the local machine for job ID 271b67ac-89fc-4d7b-921f-d3544a2686cf...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356466917354 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase7/Bootstrap
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   Estimation::PopulationEstimates::MLE
##   Estimation::PopulationEstimates::OtherMethodBootstrap
##   Estimation::PrecisionPopulationEstimates::OtherMethodBootstrap
##   Estimation::IndividualEstimates::Estimates
##   Estimation::IndividualEstimates::RandomEffects
##   Estimation::Residuals::ResidualTable
##   Estimation::Predictions
##   Estimation::OFMeasures::Deviance
## 
## The following WARNINGs were raised during the job execution:
##  bootstrap_parameter_scale: The parameters PPV_CL, CORR_CL_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:
##  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: 4.2
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 13:09:50 2016
## Warning: NAs introduced by coercion
## [[1]]

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/UseCase7_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()
## png 
##   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    RUV_ADD 
##  0.1341360  8.1009100  1.5617100  0.9677450  0.0716038  0.1932260 
##     POP_F1 BETA_CL_WT  BETA_V_WT     PPV_CL  CORR_CL_V      PPV_V 
##  1.0000000  0.7500000  1.0000000  0.2637860  0.2425380  0.1348310 
##     PPV_KA   PPV_TLAG 
##  0.9360370  0.1000000 
## 
## $Bootstrap
##             Parameter        Mean     Median
## BETA_CL_WT BETA_CL_WT 0.750000000 0.75000000
## BETA_V_WT   BETA_V_WT 1.000000000 1.00000000
## CORR_CL_V   CORR_CL_V 0.005750799 0.00564692
## POP_CL         POP_CL 0.134616500 0.13647200
## POP_F1         POP_F1 1.000000000 1.00000000
## POP_KA         POP_KA 1.505258000 1.41767500
## POP_TLAG     POP_TLAG 0.929751300 0.93550350
## POP_V           POP_V 8.112643000 8.03167000
## PPV_CL         PPV_CL 0.070234170 0.06764700
## PPV_KA         PPV_KA 0.584371900 0.54790300
## PPV_TLAG     PPV_TLAG 0.010000000 0.01000000
## PPV_V           PPV_V 0.014625210 0.01407655
## RUV_ADD       RUV_ADD 0.113435900 0.07271430
## RUV_PROP     RUV_PROP 0.093185120 0.09585170

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  0.7500000000 0.75000000
## 2   BETA_V_WT 1.000000000 1.00000000  1.0000000000 1.00000000
## 3   CORR_CL_V 0.005750799 0.00564692 -0.0041095320 0.01431807
## 4      POP_CL 0.134616500 0.13647200  0.1129542000 0.14712140
## 5      POP_F1 1.000000000 1.00000000  1.0000000000 1.00000000
## 6      POP_KA 1.505258000 1.41767500  0.8082667000 3.04456700
## 7    POP_TLAG 0.929751300 0.93550350  0.7726145000 1.00541500
## 8       POP_V 8.112643000 8.03167000  7.6788860000 8.59129700
## 9      PPV_CL 0.070234170 0.06764700  0.0241277000 0.12453910
## 10     PPV_KA 0.584371900 0.54790300  0.0005248084 1.67861400
## 11   PPV_TLAG 0.010000000 0.01000000  0.0100000000 0.01000000
## 12      PPV_V 0.014625210 0.01407655  0.0071648030 0.02859239
## 13    RUV_ADD 0.113435900 0.07271430  0.0010990000 0.32173970
## 14   RUV_PROP 0.093185120 0.09585170  0.0482136000 0.14126890

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] "0.134136"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "8.10091"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "1.56171"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.967745"
## 
## $POP_TLAG$lo
## [1] "0.001"
## 
## 
## $POP_F1
## $POP_F1$value
## [1] "1"
## 
## $POP_F1$fix
## [1] "true"
## 
## 
## $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] "0.263786"
## 
## 
## $PPV_V
## $PPV_V$value
## [1] "0.134831"
## 
## 
## $PPV_KA
## $PPV_KA$value
## [1] "0.936037"
## 
## 
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.1"
## 
## $PPV_TLAG$fix
## [1] "true"
## 
## 
## $CORR_CL_V
## $CORR_CL_V$value
## [1] "0.242538"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0716038"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.193226"
## 
## $RUV_ADD$lo
## [1] "1.0E-4"

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) 
## -- Tue Aug 16 13:10:03 2016
## New
## Submitted
## Job 0045baf2-e656-4fcb-be92-f043a53e98a4 progress:
## Running [ ... ]
## Importing Results
## Copying the result data back to the local machine for job ID 0045baf2-e656-4fcb-be92-f043a53e98a4...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35641d4960ef to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase7/VPC
## Done.
## 
## 
## The following main elements were parsed successfully:
##   RawResults
##   SimulationSimulationBlock
##   SimulationSimulationBlock
## 
## The following MESSAGEs were raised during the job execution:
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 13:11:05 2016

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()
## png 
##   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 
##  0.1341360  8.1009100  1.5617100  0.9677450  0.0716038  0.1932260 
##     POP_F1 BETA_CL_WT  BETA_V_WT     PPV_CL  CORR_CL_V      PPV_V 
##  1.0000000  0.7500000  1.0000000  0.2637860  0.2425380  0.1348310 
##     PPV_KA   PPV_TLAG     logtWT 
##  0.9360370  0.1000000  0.0000000

Simulate PK parameters for individuals

ind <- list(name = c('TLAG','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 into the GUT (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,y,f))

Simulated parameter values for each individual

print(res$parameter)
##    id      TLAG        KA         CL        V
## 1   1 0.9861218 1.4286223 0.11784582 8.886080
## 2   2 0.9190286 5.6400807 0.09214690 6.255916
## 3   3 1.3349890 2.6380193 0.13988075 7.733947
## 4   4 0.8690646 5.4701217 0.15587915 8.450390
## 5   5 0.7989207 0.7546081 0.18540489 8.077224
## 6   6 0.9718773 2.1640347 0.21814055 8.451690
## 7   7 0.7864084 2.3082393 0.23786147 9.650570
## 8   8 1.0106150 1.9142694 0.26330568 7.862231
## 9   9 0.9330995 0.5820375 0.09547058 9.231371
## 10 10 1.0270264 6.1631955 0.11633405 7.648809
## 11 11 0.9239469 1.7104466 0.12664233 7.790393
## 12 12 0.8641969 0.7722433 0.14644074 8.381979

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-47

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 prediction intervals with prctilemlx. band defines the percentile bands displayed:

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

plot of chunk unnamed-chunk-49

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 -0.4082097 -0.23807178 -0.16941383 -0.12354093 -0.0521384864
## 2   0.5 -0.4385293 -0.21043393 -0.12039753 -0.05636257 -0.0006352302
## 3   1.0 -0.4483055 -0.08173416  0.01545145  0.17644583  0.3945920942
## 4   2.0  1.7626055  4.70969568  6.60006873  8.14554916  9.0233426056
## 5   3.0  3.1906167  7.64388817  9.00825258 10.07995360 10.6581730746
## 6   4.0  5.9875491  9.08682835  9.79127423 10.41408318 10.7013207127
## 7   6.0  6.7385865  9.28328476  9.96915402 10.47738527 10.8322507287
## 8   8.0  7.0981753  8.91956526  9.60067915  9.97200076 10.4654226104
## 9  12.0  6.7208196  8.75034987  9.32405535  9.69521944 10.1068798162
## 10 24.0  4.9833753  6.66078449  7.37601106  7.59110380  8.0580755042
## 11 36.0  3.6227165  5.30493767  5.82907396  6.20056107  6.5735509557
## 12 48.0  2.4960288  4.01401757  4.67675014  5.11665442  5.3291945824
##            50%         50%         60%         70%        80%        90%
## 1  -0.02541085 -0.02541085  0.01534420  0.05938699  0.1121857  0.2409712
## 2   0.02962179  0.02962179  0.05632682  0.10796407  0.1764253  0.2417221
## 3   0.70309779  0.70309779  0.98651866  1.35306045  2.3800631  4.4976771
## 4   9.87323346  9.87323346 10.26263289 10.69957039 11.2171608 12.4155361
## 5  11.15072915 11.15072915 11.68625041 12.05954226 12.4433313 13.4505346
## 6  11.18710215 11.18710215 11.97243992 12.72223918 13.0732758 13.3815267
## 7  11.27516150 11.27516150 11.88760972 12.24949226 12.5944127 13.7529080
## 8  10.73853468 10.73853468 11.18444468 11.85425754 12.2803888 13.0106827
## 9  10.42749881 10.42749881 10.93552300 11.25362287 11.7935899 12.6484881
## 10  8.33671298  8.33671298  8.70536841  8.96546426  9.4811594 10.4515332
## 11  6.91228323  6.91228323  7.28009238  7.58347041  8.0655068  8.9144817
## 12  5.56339616  5.56339616  5.96448961  6.39165624  6.6521519  7.4024209
##          100%
## 1   0.5249969
## 2   0.4790326
## 3   7.3451095
## 4  16.3646369
## 5  16.3121626
## 6  16.2005168
## 7  16.2408663
## 8  14.9011574
## 9  14.5173087
## 10 12.6104656
## 11 11.9198117
## 12 10.3258922