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

UseCase6 : ODE model with multiple correlations

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)

Set name of .mdl file and dataset for future tasks

uc<-"UseCase6"
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] "UseCase6.mdl"      "warfarin_conc.csv"

Introduction to 'ddmore'

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_COV_par"  "warfarin_PK_VCOV_mdl"
## [4] "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 12:49:36 2016
## New
## Submitted
## Job 1d2c327f-b57b-4b79-b0c7-c3abeeacaf0b progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 1d2c327f-b57b-4b79-b0c7-c3abeeacaf0b...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35644f4612ef to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase6/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 12:50:59 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/UseCase6.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")
print(parameters_mlx)
## $MLE
##      POP_V  BETA_V_WT     POP_KA     POP_CL BETA_CL_WT   POP_TLAG 
##    8.08521    1.00000    1.49922    0.13283    0.75000    0.84769 
##      PPV_V     PPV_KA     PPV_CL   PPV_TLAG   COV_V_KA   COV_CL_V 
##    0.01680    0.50577    0.06995    0.10000   -0.25559    0.23523 
##  COV_CL_KA    RUV_ADD   RUV_PROP 
##   -0.32752    0.14046    0.08026
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   COV_CL_KA -0.32752 0.35741 109.13
## 4    COV_CL_V  0.23523 0.19874  84.49
## 5    COV_V_KA -0.25559 0.40507 158.49
## 6      POP_CL  0.13283 0.00632   4.76
## 7      POP_KA  1.49922 0.42163  28.12
## 8    POP_TLAG  0.84769 0.12035  14.20
## 9       POP_V  8.08521 0.21572   2.67
## 10     PPV_CL  0.06995 0.01810  25.88
## 11     PPV_KA  0.50577 0.32413  64.09
## 12   PPV_TLAG  0.10000 0.00000   0.00
## 13      PPV_V  0.01680 0.00565  33.66
## 14    RUV_ADD  0.14046 0.03490  24.85
## 15   RUV_PROP  0.08026 0.00843  10.51
print(getEstimationInfo(mlx))
## $OFMeasures
## $OFMeasures$LogLikelihood
## $OFMeasures$LogLikelihood[[1]]
## [1] -319.985
## 
## 
## $OFMeasures$IndividualContribToLL
##    Subject ICtoLL
## 1        1 -18.79
## 2        2  -5.11
## 3        3 -12.21
## 4        4 -12.01
## 5        5 -10.93
## 6        6  -7.36
## 7        7 -21.17
## 8        8 -21.49
## 9        9 -30.55
## 10      10  -5.72
## 11      12 -18.92
## 12      13 -14.90
## 13      14 -16.61
## 14      15 -12.33
## 15      16 -13.40
## 16      17  -5.60
## 17      18  -4.98
## 18      19  -7.07
## 19      20  -5.07
## 20      21  -5.92
## 21      22  -6.00
## 22      23  -8.00
## 23      24  -4.75
## 24      25  -7.30
## 25      26  -7.25
## 26      27  -5.23
## 27      28  -6.78
## 28      29  -5.51
## 29      30  -4.46
## 30      31  -4.95
## 31      32  -4.63
## 32      33  -4.97
## 
## $OFMeasures$InformationCriteria
## $OFMeasures$InformationCriteria$AIC
## [1] 663.97
## 
## $OFMeasures$InformationCriteria$BIC
## [1] 681.56
## 
## 
## 
## $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)

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()
## 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 12:51:04 2016
## New
## Submitted
## Job 1fa33f48-4900-406e-a0ed-bd63fc1d4fcc progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 1fa33f48-4900-406e-a0ed-bd63fc1d4fcc...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356437e52eaa to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase6/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 12:52:26 2016

Load previous results

NM <- LoadSOObject(“NONMEM/UseCase6.SO.xml”)

parameters_nm <- getPopulationParameters(NM, what="estimates")
print(parameters_nm)
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##  1.32208e-01  8.14851e+00  1.33317e+00  7.49357e-01  1.12517e-01 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       PPV_CL     COV_CL_V 
##  2.01662e-09  7.50000e-01  1.00000e+00  6.68421e-02  9.75709e-03 
##        PPV_V    COV_CL_KA     COV_V_KA       PPV_KA     PPV_TLAG 
##  1.69028e-02 -6.19098e-02 -3.27767e-02  4.32279e-01  1.00000e-01
print(parameters_mlx)
## $MLE
##      POP_V  BETA_V_WT     POP_KA     POP_CL BETA_CL_WT   POP_TLAG 
##    8.08521    1.00000    1.49922    0.13283    0.75000    0.84769 
##      PPV_V     PPV_KA     PPV_CL   PPV_TLAG   COV_V_KA   COV_CL_V 
##    0.01680    0.50577    0.06995    0.10000   -0.25559    0.23523 
##  COV_CL_KA    RUV_ADD   RUV_PROP 
##   -0.32752    0.14046    0.08026
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -437.0003
## 
## 
## 
## $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-21

print(ind.plots(nm.xpdb))

plot of chunk unnamed-chunk-21 plot of chunk unnamed-chunk-21

print(parm.hist(nm.xpdb))

plot of chunk unnamed-chunk-21

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 12:52:41 2016
## New
## Submitted
## Job 035942f6-319c-4fb9-9555-b1d8322917d6 progress:
## Running [ .. ]
## Importing Results
## Copying the result data back to the local machine for job ID 035942f6-319c-4fb9-9555-b1d8322917d6...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35645efe6c13 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase6/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: 3.2
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 12:53:23 2016

Load previous results

NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase6_FOCEI.SO.xml”)

Results from NONMEM should be comparable to previous results

getPopulationParameters(NM.FOCEI,  what="estimates")
## $MLE
##      POP_CL       POP_V      POP_KA    POP_TLAG    RUV_PROP     RUV_ADD 
##  0.13330800  8.09650000  2.42977000  0.87078700  0.08588440  0.10681000 
##  BETA_CL_WT   BETA_V_WT      PPV_CL    COV_CL_V       PPV_V   COV_CL_KA 
##  0.75000000  1.00000000  0.06944200  0.00785012  0.01748590 -0.10244000 
##    COV_V_KA      PPV_KA    PPV_TLAG 
##  0.03209470  1.26856000  0.10000000
parameters_nm
## $MLE
##       POP_CL        POP_V       POP_KA     POP_TLAG     RUV_PROP 
##  1.32208e-01  8.14851e+00  1.33317e+00  7.49357e-01  1.12517e-01 
##      RUV_ADD   BETA_CL_WT    BETA_V_WT       PPV_CL     COV_CL_V 
##  2.01662e-09  7.50000e-01  1.00000e+00  6.68421e-02  9.75709e-03 
##        PPV_V    COV_CL_KA     COV_V_KA       PPV_KA     PPV_TLAG 
##  1.69028e-02 -6.19098e-02 -3.27767e-02  4.32279e-01  1.00000e-01

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-26

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 12:53:26 2016
## New
## Submitted
## Job 7121a2c6-591d-441c-a59d-db3b6e74c7ce progress:
## Running [ ........ ]
## Importing Results
## Copying the result data back to the local machine for job ID 7121a2c6-591d-441c-a59d-db3b6e74c7ce...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356421c95e9a to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase6/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 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: 3.2
##  nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
## 
## Completed
## -- Tue Aug 16 12:56:10 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/UseCase6_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 
##  0.13330800  8.09650000  2.42977000  0.87078700  0.08588440  0.10681000 
##  BETA_CL_WT   BETA_V_WT      PPV_CL    COV_CL_V       PPV_V   COV_CL_KA 
##  0.75000000  1.00000000  0.06944200  0.00785012  0.01748590 -0.10244000 
##    COV_V_KA      PPV_KA    PPV_TLAG 
##  0.03209470  1.26856000  0.10000000 
## 
## $Bootstrap
##             Parameter         Mean       Median
## BETA_CL_WT BETA_CL_WT  0.750000000  0.750000000
## BETA_V_WT   BETA_V_WT  1.000000000  1.000000000
## COV_CL_KA   COV_CL_KA -0.055342372 -0.061038850
## COV_CL_V     COV_CL_V  0.005459428  0.004788685
## COV_V_KA     COV_V_KA  0.025764330  0.023972250
## POP_CL         POP_CL  0.134930900  0.136003000
## POP_KA         POP_KA  1.826123000  1.599315000
## POP_TLAG     POP_TLAG  0.860107900  0.846569500
## POP_V           POP_V  8.061713000  7.998770000
## PPV_CL         PPV_CL  0.073844440  0.079305700
## PPV_KA         PPV_KA  0.720084800  0.652388000
## PPV_TLAG     PPV_TLAG  0.100000000  0.100000000
## PPV_V           PPV_V  0.015463340  0.015415500
## RUV_ADD       RUV_ADD  0.103770200  0.127975500
## RUV_PROP     RUV_PROP  0.090400720  0.087055850

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  0.750000000  0.750000000 0.75000000
## 2   BETA_V_WT  1.000000000  1.000000000  1.000000000 1.00000000
## 3   COV_CL_KA -0.055342372 -0.061038850 -0.276082850 0.08568050
## 4    COV_CL_V  0.005459428  0.004788685 -0.004479309 0.01480269
## 5    COV_V_KA  0.025764330  0.023972250 -0.069446230 0.13410720
## 6      POP_CL  0.134930900  0.136003000  0.124275900 0.14685000
## 7      POP_KA  1.826123000  1.599315000  0.867710300 4.01230900
## 8    POP_TLAG  0.860107900  0.846569500  0.720892900 1.00746700
## 9       POP_V  8.061713000  7.998770000  7.694330000 8.53056800
## 10     PPV_CL  0.073844440  0.079305700  0.029616610 0.12524730
## 11     PPV_KA  0.720084800  0.652388000  0.187502800 1.44519200
## 12   PPV_TLAG  0.100000000  0.100000000  0.100000000 0.10000000
## 13      PPV_V  0.015463340  0.015415500  0.008167550 0.02678049
## 14    RUV_ADD  0.103770200  0.127975500  0.001000000 0.26657310
## 15   RUV_PROP  0.090400720  0.087055850  0.058423380 0.13516620

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] "0.133308"
## 
## $POP_CL$lo
## [1] "0.001"
## 
## 
## $POP_V
## $POP_V$value
## [1] "8.0965"
## 
## $POP_V$lo
## [1] "0.001"
## 
## 
## $POP_KA
## $POP_KA$value
## [1] "2.42977"
## 
## $POP_KA$lo
## [1] "0.001"
## 
## 
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.870787"
## 
## $POP_TLAG$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] "0.069442"
## 
## 
## $PPV_V
## $PPV_V$value
## [1] "0.0174859"
## 
## 
## $PPV_KA
## $PPV_KA$value
## [1] "1.26856"
## 
## 
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.1"
## 
## $PPV_TLAG$fix
## [1] "true"
## 
## 
## $COV_CL_V
## $COV_CL_V$value
## [1] "0.00785012"
## 
## 
## $COV_CL_KA
## $COV_CL_KA$value
## [1] "-0.10244"
## 
## 
## $COV_V_KA
## $COV_V_KA$value
## [1] "0.0320947"
## 
## 
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0858844"
## 
## $RUV_PROP$lo
## [1] "0"
## 
## 
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.10681"
## 
## $RUV_ADD$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) 
## -- Tue Aug 16 12:56:21 2016
## New
## Submitted
## Job e14f9c34-8259-4df5-b11a-19987c045372 progress:
## Running [ ... ]
## Importing Results
## Copying the result data back to the local machine for job ID e14f9c34-8259-4df5-b11a-19987c045372...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356413f7692c to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase6/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 12:57:24 2016

plot of chunk unnamed-chunk-35

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 
##  0.13330800  8.09650000  2.42977000  0.87078700  0.08588440  0.10681000 
##  BETA_CL_WT   BETA_V_WT      PPV_CL    COV_CL_V       PPV_V   COV_CL_KA 
##  0.75000000  1.00000000  0.06944200  0.00785012  0.01748590 -0.10244000 
##    COV_V_KA      PPV_KA    PPV_TLAG      logtWT 
##  0.03209470  1.26856000  0.10000000  0.00000000

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

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

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 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      TLAG         KA         CL         V
## 1   1 0.6621457  2.5337911 0.14827127  7.842324
## 2   2 1.4003137  1.5811096 0.12319921  6.503757
## 3   3 0.9899196 92.1657397 0.11367485  9.224591
## 4   4 0.8344262  0.9841584 0.11381968  8.167405
## 5   5 0.9961214  0.4464952 0.10641424  8.438860
## 6   6 0.9520886  2.9135404 0.12152391 10.030840
## 7   7 0.7155444  0.6161061 0.07639769  9.157918
## 8   8 1.2204339  5.0839285 0.11025531 10.919296
## 9   9 0.5559791  1.5414754 0.13975542  6.942445
## 10 10 0.9455286  4.4992913 0.13442991  7.755041
## 11 11 0.9353887  0.8747598 0.19119774  8.038124
## 12 12 0.8302670  0.5957391 0.15364599  8.190014

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-46

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,f,y))

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

print(prctilemlx(res.1000$CC,band=list(number=10, level=100)))

plot of chunk unnamed-chunk-48

Plot of observed concentrations (with residual error)

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

plot of chunk unnamed-chunk-49

Table of the same information

prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=F)$y
##    time         0%         10%          20%         30%          40%
## 1   0.0 -0.2256470 -0.13159951 -0.093647288 -0.06829002 -0.028820716
## 2   0.5 -0.2424069 -0.11632207 -0.062097853 -0.02772178  0.003150656
## 3   1.0 -0.2119107 -0.04433479 -0.004253361  0.09734471  0.412134400
## 4   2.0  2.0159184  4.42603243  6.740490545  8.55001243  9.445620815
## 5   3.0  4.1244757  7.50562059  9.262435997  9.99395562 10.715040238
## 6   4.0  4.5577413  9.07240187  9.863678890 10.30557787 11.095077276
## 7   6.0  6.7795443  8.97745151  9.752382357 10.01874601 10.494306232
## 8   8.0  6.8851238  8.79986155  9.417065333  9.82250231 10.299517337
## 9  12.0  7.2330724  8.77570039  9.418782379  9.64336299  9.907050773
## 10 24.0  5.4330543  6.92414065  7.528376882  7.79329141  7.935608942
## 11 36.0  3.9758516  5.35726544  6.080274191  6.24896542  6.696433002
## 12 48.0  2.5216985  4.37791186  4.857501171  5.20154397  5.477645204
##            50%         50%         60%         70%         80%        90%
## 1  -0.01404642 -0.01404642  0.00848185  0.03282749  0.06201317  0.1332022
## 2   0.01757170  0.01757170  0.04240292  0.06877119  0.10782657  0.1524774
## 3   0.96294909  0.96294909  2.08865215  4.69199013  6.07504750  9.4316502
## 4  10.36904340 10.36904340 10.95811945 11.36252148 12.03918758 13.4247926
## 5  11.32247005 11.32247005 11.84495245 12.37712008 12.91786323 13.7973035
## 6  11.41485788 11.41485788 11.98341622 12.27754952 13.02976315 13.8307028
## 7  11.04616564 11.04616564 11.73835513 12.36834065 13.19768046 14.0895574
## 8  10.75752318 10.75752318 11.23879625 11.77174418 12.22186683 12.7527160
## 9  10.25564662 10.25564662 11.01328786 11.38102453 11.81121097 12.4055270
## 10  8.36160658  8.36160658  8.66446943  9.17932599  9.64929903 10.0992910
## 11  6.93470174  6.93470174  7.30385439  7.83449599  8.22882864  9.2161280
## 12  5.80713747  5.80713747  6.12145226  6.42040626  6.87337403  7.2333707
##          100%
## 1   0.2902038
## 2   3.0705034
## 3  13.3925594
## 4  17.0051562
## 5  18.8070194
## 6  17.2683970
## 7  16.0918247
## 8  14.6069312
## 9  15.4452127
## 10 12.6878997
## 11 10.3956880
## 12  8.1542338