```
library(mrgsolve)
options(mrgsolve.soloc="build")
<- mread_cache("pred1", modlib()) mod
```

# 1 Introduction

This post introduces a new formal code block for writing models where there are no compartments. The block is named after the analogous NONMEM block called `$PRED`

. This functionality has always been possible with mrgsolve, but only now is there a code block dedicated to these models. Also, a relaxed set of data set constraints have been put in place when these types of models are invoked.

# 2 Example model

As a most-basic model, we look at the `pred1`

model in `modlib()`

The model code is

```
$PROB
An example model expressed in closed form= -1, beta0 = 100, beta1 = 0.1
$PARAM B 2 0.3
$OMEGA
$PREDdouble beta0i = beta0 + ETA(1);
double beta1i = beta1*exp(ETA(2));
= beta0i + beta1i*B; capture Y
```

This is a random-intercept, random slope linear model. Like other models in mrgsolve, you can write parameters (`$PARAM`

), and random effects (`$OMEGA`

). But the model is actually written in `$PRED`

.

When mrgsolve finds `$PRED`

, it will generate an error if it also finds `$MAIN`

, `$TABLE`

, or `$ODE`

. However, the code that gets entered into `$PRED`

would function exactly as if you put it in `$TABLE`

.

In the example model, the response is a function of the parameter `B`

, so we’ll generate an input data set with some values of `B`

```
library(dplyr)
<- tibble(ID = 1, B = exp(rnorm(100, 0,2)))
data
head(data)
```

```
# A tibble: 6 × 2
ID B
<dbl> <dbl>
1 1 6.72
2 1 5.68
3 1 0.171
4 1 6.01
5 1 0.198
6 1 1.93
```

```
<- mrgsim_d(mod,data,carry.out="B")
out
plot(out, Y~B)
```

Like other models, we can simulate from a population

```
library(purrr)
set.seed(223)
<- map_df(1:30, function(i) tibble(ID = i, B = seq(0,30,1)))
df
head(df)
```

```
# A tibble: 6 × 2
ID B
<int> <dbl>
1 1 0
2 1 1
3 1 2
4 1 3
5 1 4
6 1 5
```

```
%>%
mod data_set(df) %>%
mrgsim(carry.out="B") %>%
plot(Y ~ B)
```

# 3 PK/PD Model

Here is an implementation of a PK/PD model using `$PRED`

In this model

- Calculate
`CL`

as a function of`WT`

and a random effect - Derive
`AUC`

from`CL`

and`DOSE`

- The response (
`Y`

) is a calculated from`AUC`

and the Emax model parameters

```
<- '
code $PARAM TVCL = 1, WT = 70, AUC50 = 20, DOSE = 100, E0 = 35, EMAX = 2.4
$OMEGA 1
$SIGMA 100
$PRED
double CL = TVCL*pow(WT/70,0.75)*exp(ETA(1));
capture AUC = DOSE/CL;
capture Y = E0*(1+EMAX*AUC/(AUC50+AUC))+EPS(1);
'
```

`<- mcode_cache("pkpd", code) mod `

`Building pkpd ... done.`

To simulate, look at 50 subjects at each of 5 doses

```
<-
data expand.idata(DOSE = c(30,50,80,110,200),ID = 1:50) %>%
mutate(WT = exp(rnorm(n(),log(80),1)))
head(data)
```

```
ID DOSE WT
1 1 30 59.13254
2 2 50 317.32739
3 3 80 242.15746
4 4 110 170.78136
5 5 200 248.18054
6 6 30 51.22012
```

```
<- mrgsim_d(mod,data,carry.out="WT,DOSE") %>% as.data.frame
out
head(out)
```

```
ID time WT DOSE AUC Y
1 1 0 59.13254 30 231.90852 110.61330
2 2 0 317.32739 50 36.76051 85.27834
3 3 0 242.15746 80 36.54808 98.90407
4 4 0 170.78136 110 23.68354 80.29131
5 5 0 248.18054 200 331.10229 108.81926
6 6 0 51.22012 30 251.58373 116.09649
```

Plot the response (`Y`

) versus `AUC`

, colored by dose

```
library(ggplot2)
ggplot(out, aes(AUC,Y,col =factor(DOSE))) +
geom_point() +
scale_x_continuous(trans = "log", breaks = 10^seq(-4,4)) +
geom_smooth(aes(AUC,Y),se = FALSE,col="darkgrey") + theme_bw() +
scale_color_brewer(palette = "Set2", name = "") +
theme(legend.position = "top")
```

``geom_smooth()` using method = 'loess' and formula = 'y ~ x'`