# Introduction to PopED

#### 2016-12-12

PopED computes optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix (FIM).

To get started you need to define

• A model.
• An initial design (and design space if you want to optimize)

Below is an example to introduce the package. There are several other examples, as r-scripts, in the “examples” folder in the PopED installation directory located at:

system.file("examples", package="PopED")

## Define a model

Here we define a one-compartment pharmacokinetic model with linear absorption using an analytical solution. In this case the solution is applicable for both multiple and single dosing. Note that this function is also predefined in PopED as ff.PK.1.comp.oral.md.CL.

library(PopED)
ff <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
N = floor(xt/TAU)+1
y=(DOSE*Favail/V)*(KA/(KA - CL/V)) *
(exp(-CL/V * (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) -
exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
return(list( y=y,poped.db=poped.db))
})
}

Next we define the parameters of this function, in this case log-normal distributions around the parameters (not Favail). DOSE and TAU are defined as a covariates (a) so that we can optimize these values later.

sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
}

Now we define the residual unexplained variability (RUV) function, in this case the RUV has both an additive and proportional component.

feps <- function(model_switch,xt,parameters,epsi,poped.db){
returnArgs <- ff(model_switch,xt,parameters,poped.db)
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]

y = y*(1+epsi[,1])+epsi[,2]

return(list(y=y,poped.db=poped.db))
}

We have now defined all aspects of the model.

## Define model parameter values, an initial design and a design space for optimization

Now we define the model parameter values, the initial design and design space for optimization.

In this example, the parameter values are defined for the fixed effects (bpop), the between-subject variability variances (d) and the residual variability variances (sigma). We also fix the parameter Favail using notfixed_bpop, since we have only oral dosing and the parameter is not identifiable. Fixing a parameter means that we assume the parameter will not be estimated. In addition, we fix the small additive RUV term, as this term is reflecting the higher error expected at low concentration measurements (limit of quantification measurements) and would typically be calculated from analytical assay methods (for example, the standard deviation of the parameter might be 20% of the limit of quantification).

For the initial design, we define two groups (m=2) of 20 individuals (groupsize=20), with doses of 20 mg or 40 mg every 24 hours (a). The initial design has 5 sample times per individual (xt). For the design space, which can be searched during optimization, we define a potential dose range of between 0 and 200 mg (mina and maxa), and a range of potential sample times between 0 and 10 hours for the first three samples and between 240 and 248 hours for the last two samples (minxt and maxxt). Finally, we fix the two groups of subjects to have the sample sample times (bUseGrouped_xt=TRUE).

poped.db <- create.poped.database(ff_fun=ff,
fg_fun=sfg,
fError_fun=feps,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9),
notfixed_bpop=c(1,1,1,0),
d=c(V=0.09,KA=0.09,CL=0.25^2),
sigma=c(0.04,5e-6),
notfixed_sigma=c(1,0),
m=2,
groupsize=20,
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24)),
maxa=c(DOSE=200,TAU=24),
mina=c(DOSE=0,TAU=24),
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
bUseGrouped_xt=TRUE)

### Simulation

First it may make sense to check your model and design to make sure you get what you expect when simulating data. Here we plot the model typical values:

plot_model_prediction(poped.db, model_num_points = 500)

Next, we plot the model typical values prediction intervals taking into account the between-subject variability (you can even investigate the effects of the residual, unexplained, variability with the DV=T argument):

plot_model_prediction(poped.db, model_num_points=500, IPRED=T)

We can get these predictions numerically as well:

dat <- model_prediction(poped.db,DV=T)
##   ID Time         DV      IPRED       PRED Group Model DOSE TAU
## 1  1    1 0.04422501 0.05747039 0.05325024     1     1   20  24
## 2  1    2 0.08522768 0.09828859 0.09204804     1     1   20  24
## 3  1    8 0.11048943 0.17340393 0.16409609     1     1   20  24
## 4  1  240 0.17322493 0.22959440 0.12671376     1     1   20  24
## 5  1  245 0.46941713 0.35619950 0.24980320     1     1   20  24
##     ID Time        DV     IPRED      PRED Group Model DOSE TAU
## 196 40    1 0.1500322 0.1613167 0.1065005     2     1   40  24
## 197 40    2 0.2746268 0.2762786 0.1840961     2     1   40  24
## 198 40    8 0.4568408 0.4497718 0.3281922     2     1   40  24
## 199 40  240 0.2097838 0.1769645 0.2534275     2     1   40  24
## 200 40  245 0.6648005 0.5527004 0.4996064     2     1   40  24

### Design evaluation

Next, we evaluate the initial design

evaluate_design(poped.db)
## $ofv ## [1] 39.309 ## ##$fim
##             [,1]        [,2]         [,3]       [,4]       [,5]
## [1,]  0.05336692   -8.683963  -0.05863412   0.000000   0.000000
## [2,] -8.68396266 2999.851007 -14.43058560   0.000000   0.000000
## [3,] -0.05863412  -14.430586  37.15243290   0.000000   0.000000
## [4,]  0.00000000    0.000000   0.00000000 999.953587 312.240246
## [5,]  0.00000000    0.000000   0.00000000 312.240246 439.412556
## [6,]  0.00000000    0.000000   0.00000000   3.202847   2.287838
## [7,]  0.00000000    0.000000   0.00000000 575.347261 638.581909
##             [,6]       [,7]
## [1,]    0.000000     0.0000
## [2,]    0.000000     0.0000
## [3,]    0.000000     0.0000
## [4,]    3.202847   575.3473
## [5,]    2.287838   638.5819
## [6,] 3412.005199  1182.3255
## [7,] 1182.325475 33864.3226
##
## $rse ## bpop[1] bpop[2] bpop[3] D[1,1] D[2,2] D[3,3] ## 8.215338 10.090955 4.400304 39.844763 60.655110 27.562541 ## SIGMA[1,1] ## 13.865357 We see that the fixed-effect and residual variability parameters are relatively well estimated with this design, but the between-subject variability parameters are less well estimated. ### Design optimization Now we can optimize the sample times of the design1. output <- poped_optim(poped.db, opt_xt=T) summary(output) ## =============================================================================== ## FINAL RESULTS ## Optimized Sampling Schedule ## Group 1 : 0.4574 10 10 240 240.9 ## Group 2 : 0.4574 10 10 240 240.9 ## ## OFV = 40.5277 ## ## Efficiency: ## ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = 1.1902 ## ## Expected parameter ## relative standard error (%RSE): ## Parameter Values RSE_0 RSE ## bpop[1] 72.8000 8.22 6.28 ## bpop[2] 0.2500 10.09 7.74 ## bpop[3] 3.7500 4.40 4.29 ## D[1,1] 0.0900 39.84 32.54 ## D[2,2] 0.0900 60.66 50.44 ## D[3,3] 0.0625 27.56 26.44 ## SIGMA[1,1] 0.0400 13.87 14.80 ## ## Total running time: 36.229 seconds plot_model_prediction(output$poped.db)

We see that there are four distinct sample times for this design. This means that for this model, with these exact parameter values, that the most information from the study to inform the parameter estimation is with these sample times.

### Examine efficiency of sampling windows

Of course, this means that there are multiple samples at some of these time points. We can explore a more practical design by looking at the loss of efficiency if we spread out sample times in a uniform distribution around these optimal points ($$\pm 30$$ minutes).

plot_efficiency_of_windows(output$poped.db,xt_windows=0.5) Here we see the efficiency ($$(|FIM_{optimized}|/|FIM_{initial}|)^{1/npar}$$) drop below 80% in some cases, which is mostly caused by an increase in the D[2,2] parameter uncertainty (BSV on absorption). Smaller windows or different windowing on different samples may be in order here. To investigate see ?plot_efficiency_of_windows. ## Optimize over a discrete design space In the previous example we optimized over a continuous design space (sample times could be optimized to be any value between a lower and an upper limit). We could also limit the search to only “allowed” values, for example, only samples taken on the hour are allowed. poped.db.discrete <- create.poped.database(poped.db,discrete_xt = list(0:248)) output_discrete <- poped_optim(poped.db.discrete, opt_xt=T) summary(output_discrete) ## =============================================================================== ## FINAL RESULTS ## Optimized Sampling Schedule ## Group 1 : 1 10 10 240 241 ## Group 2 : 1 10 10 240 241 ## ## OFV = 40.3782 ## ## Efficiency: ## ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = 1.165 ## ## Expected parameter ## relative standard error (%RSE): ## Parameter Values RSE_0 RSE ## bpop[1] 72.8000 8.22 6.33 ## bpop[2] 0.2500 10.09 8.01 ## bpop[3] 3.7500 4.40 4.30 ## D[1,1] 0.0900 39.84 32.46 ## D[2,2] 0.0900 60.66 53.33 ## D[3,3] 0.0625 27.56 26.52 ## SIGMA[1,1] 0.0400 13.87 14.68 ## ## Total running time: 15.977 seconds plot_model_prediction(output_discrete$poped.db)

Here we see that the optimization ran somewhat quicker, but gave a less efficient design.

## Optimize ‘Other’ design variables

One could also optimize over dose, to see if a different dose could help in parameter estimation .

output_dose_opt <- poped_optim(output$poped.db, opt_xt=T, opt_a=T) In this case the results are predictable … higher doses give observations with somewhat lower absolute residual variability leading to both groups at the highest allowed dose levels (200 mg in this case). ## Cost function to optimize dose Optimizing the dose of a study just to have better model parameter estimates may be somewhat implausible. Instead, let’s use a cost function to optimize dose based on some sort of target concentration … perhaps typical population trough concentrations of 0.2 and 0.35 for the two groups of patients at 240 hours. First we define the criteria we use to optimize the doses, here a least squares minimization. crit_fcn <- function(poped.db,...){ pred_df <- model_prediction(poped.db) sum((pred_df[pred_df["Time"]==240,"PRED"] - c(0.2,0.35))^2) } crit_fcn(output$poped.db)
## [1] 0.01469712

Now we minimize the cost function

output_cost <- poped_optim(poped.db, opt_a =T, opt_xt = F,
ofv_fun=crit_fcn,
maximize = F)
summary(output_cost)
## ===============================================================================
## FINAL RESULTS
##
## Optimized Covariates:
## Group 1: 31.5672 : 24
## Group 2: 55.2426 : 24
##
## OFV = 7.70372e-34
##
## Efficiency:
##   (ofv_final / ofv_init) = 5.2417e-32
##
## Expected parameter
## relative standard error (%RSE):
##     Parameter    Values   RSE_0     RSE
##       bpop[1]   72.8000    8.22    8.21
##       bpop[2]    0.2500   10.09   10.07
##       bpop[3]    3.7500    4.40    4.40
##        D[1,1]    0.0900   39.84   39.81
##        D[2,2]    0.0900   60.66   60.48
##        D[3,3]    0.0625   27.56   27.55
##    SIGMA[1,1]    0.0400   13.87   13.80
##
## Total running time: 5.26 seconds
get_rse(output_cost$FIM, output_cost$poped.db)
##    bpop[1]    bpop[2]    bpop[3]     D[1,1]     D[2,2]     D[3,3]
##   8.207404  10.070674   4.399378  39.808012  60.478363  27.549531
## SIGMA[1,1]
##  13.800551
plot_model_prediction(output_cost\$poped.db)

1. Tip: to make the optimization run faster use the option parallel = T in the poped_optim command.