Infusions

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.

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.

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.

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.

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

Our persisted drug model would look like this:

model

## [MAIN]
## CL=THETA_CL*exp(ETA_CL)
## V1=THETA_V1*exp(ETA_V1)
## V2=THETA_V2*exp(ETA_V2)
## Q=THETA_Q*exp(ETA_Q)
## S1=V1
## D1=THETA_D1*exp(ETA_D1)
## 
## [ODE]
## d/dt(A_CENTRAL)=Q*A_PERIPHERAL/V2 + (-CL/V1 - Q/V1)*A_CENTRAL
## d/dt(A_PERIPHERAL)=-Q*A_PERIPHERAL/V2 + Q*A_CENTRAL/V1
## d/dt(A_OUTPUT)=CL*A_CENTRAL/V1
## F=A_CENTRAL/S1
## 
## [DURATION]
## A_CENTRAL=D1
## 
## [ERROR]
## CONC=F
## CONC_ERR=CONC*(EPS_PROP + 1)
## 
## 
## THETA's:
##   name index value   fix
## 1   CL     1     5 FALSE
## 2   V1     2    80 FALSE
## 3   V2     3    20 FALSE
## 4    Q     4     4 FALSE
## 5   D1     5     5 FALSE
## OMEGA's:
##   name index index2  value   fix type
## 1   CL     1      1  0.025 FALSE  var
## 2   V1     2      2  0.025 FALSE  var
## 3   V2     3      3  0.025 FALSE  var
## 4    Q     4      4  0.025 FALSE  var
## 5   D1     5      5 20.000 FALSE  cv%
## SIGMA's:
##   name index index2 value   fix type
## 1 PROP     1      1 0.025 FALSE  var
## No variance-covariance matrix
## 
## Compartments:
## A_CENTRAL (CMT=1)
## A_PERIPHERAL (CMT=2)
## A_OUTPUT (CMT=3)

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

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

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:

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

Then, we can inject them into the dataset.

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:

ds2 %>% export(dest="RxODE") %>% dosingOnly() %>% head()

## # A tibble: 6 × 9
##      ID   ARM  TIME  EVID   MDV   AMT   CMT  RATE DOSENO
##   <dbl> <int> <dbl> <int> <int> <dbl> <int> <dbl>  <int>
## 1     1     0     0     1     1  1000     1  226.      1
## 2     2     0     0     1     1  1000     1  193.      1
## 3     3     0     0     1     1  1000     1  236.      1
## 4     4     0     0     1     1  1000     1  146.      1
## 5     5     0     0     1     1  1000     1  187.      1
## 6     6     0     0     1     1  1000     1  235.      1

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

results_d1 <- model_suite$nonmem$advan4_trans4 %>% simulate(dataset=ds2, seed=1)
shadedPlot(results_d1, "CONC")