---
title: "Infusions"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Infusions}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, results='hide', echo=F, message=F, warning=F}
library(campsis)
```

There are 2 ways to implement infusions in CAMPSIS:

* in the model: infusion duration or rate is defined for each compartment
* in the dataset: infusion duration or rate is defined for infusion

In the first case, the simulation engine will take care of the infusion duration or rate (RATE in dataset will be -1 or -2).
In the second case, CAMPSIS will inject specific values in the RATE column of the dataset.

### Infusion duration or rate implemented in model

Let's use a 2-compartment model without absorption compartment to illustrate how this can be achieved.

```{r}
model <- model_suite$nonmem$advan3_trans4
```

For this example, we're going to define a lag time `D1` for this absorption compartment.

First let's create a new parameter `D1`, log-normally distributed with a median of 5 hours and 20% CV.

```{r}
model <- model %>% add(Theta(name="D1", value=5))
model <- model %>% add(Omega(name="D1", value=20, type="cv%"))
```

Now, let's add an equation to the drug model to define `D1`.
```{r}
model <- model %>% add(Equation("D1", "THETA_D1*exp(ETA_D1)"))
```

Finally, we need to tell CAMPSIS that `D1` corresponds the infusion duration for the first compartment.

```{r}
model <- model %>% add(InfusionDuration(compartment=1, rhs="D1"))
```

Our persisted drug model would look like this:

```{r}
model
```

Now, let's infuse 1000 mg and run the simulation.

```{r}
ds1 <- Dataset(50) %>% 
  add(Infusion(time=0, amount=1000)) %>%
  add(Observations(times=seq(0,24,by=0.5)))
```


```{r infusion_model , fig.align='center', fig.height=4, fig.width=8, message=F}
results_d1 <- model %>% simulate(dataset=ds1, seed=1)
shadedPlot(results_d1, "CONC")
```

### Infusion duration or rate implemented in dataset

The same simulation can be performed by defining the infusion duration in the dataset.

For this, we need to sample `D1` values. This can be done as follows:

```{r, results='hide', echo=F, message=F, warning=F}
set.seed(1)
```

```{r}
distribution <- ParameterDistribution(model=model, theta="D1", omega="D1")
d1Values <- (distribution %>% sample(as.integer(50)))@sampled_values
```

Then, we can inject them into the dataset.

```{r}
ds2 <- Dataset(50) %>%
  add(Infusion(time=0, amount=1000, duration=d1Values)) %>%
  add(Observations(times=seq(0,24,by=0.5)))
```

Here is an overview of the dataset in its table form if we filter on the doses:

```{r}
ds2 %>% export(dest="RxODE") %>% dosingOnly() %>% head()
```

Let's now simulate this dataset using the original model.

```{r infusion_dataset , fig.align='center', fig.height=4, fig.width=8, message=F}
results_d1 <- model_suite$nonmem$advan4_trans4 %>% simulate(dataset=ds2, seed=1)
shadedPlot(results_d1, "CONC")
```
