Integrating simulx in a workflow a PKPD example
Marc Lavielle & Matthias Machacek April 18th, 2017 Â
The R codes and the Mlxtran model files can be found here
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Introduction
In this example,
modeling of the warfarin PKPD data is performed first with
Monolix,simulation of PKPD data is then performed with
simulx, using the parameters estimated byMonolix.
Define the project to be used for the simulations (relative paths)
Examples
Example 1
Simulate model with individual parameters sampled from the estimated population distribution
- Covariate WEIGHT is taken from data set
- Dosage regimen and observation times are taken from data set
plot the results ‘’as usual’’
library(gridExtra)
plot1 <- ggplot() + geom_point(data=res$y1, aes(x=time, y=y1, colour=id)) +
geom_line( data=res$y1, aes(x=time, y=y1, colour=id)) +
theme(legend.position="none") + ylab("concentration (mg/l)")
plot2 <- ggplot() + geom_point(data=res$y2, aes(x=time, y=y2, colour=id)) +
geom_line( data=res$y2, aes(x=time, y=y2, colour=id)) +
theme(legend.position="none") + ylab("PCA (%)")
grid.arrange(plot1,plot2,ncol=2)
```
Example 2
- Estimated individual parameters (conditional modes) are used the simulations
- Dosage regimen and observation times are taken from data set
res <- simulx( project = project.file,
parameter = 'mode')
plot1 <- ggplot() +
geom_point(data=res$y1, aes(x=time, y=y1, colour=id)) +
geom_line( data=res$y1, aes(x=time, y=y1, colour=id)) +
theme(legend.position="none") + ylab("concentration(mg/l)")
plot2 <- ggplot() +
geom_point(data=res$y2, aes(x=time, y=y2, colour=id)) +
geom_line( data=res$y2, aes(x=time, y=y2, colour=id)) +
theme(legend.position="none") + ylab("PCA (%)")
grid.arrange(plot1,plot2,ncol=2)
Example 3
- simulate a trial with N=20 individuals: the designs and the weights of these N patients are sampled from the original dataset with replacement
- and define predicted PK and PD as outputs
- and specify the administration schedule
N <- 20
adm <- list( amount = 25, time = seq(-240, 96, by=24 ));
out <- list( name = c('Cc','E'), time = seq(0, 200, by=0.5));
res <- simulx( project = project.file,
output = out,
treatment = adm,
group = list(size=N))
plot1 <- ggplot() + geom_point(data=res$y1, aes(x=time, y=y1, colour=id)) +
geom_line( data=res$Cc, aes(x=time, y=Cc, colour=id)) +
theme(legend.position="none") + ylab("concentration (mg/l)")
plot2 <- ggplot() + geom_point(data=res$y2, aes(x=time, y=y2, colour=id)) +
geom_line( data=res$E, aes(x=time, y=E, colour=id)) +
theme(legend.position="none") + ylab("PCA (%)")
grid.arrange(plot1,plot2,ncol=2)