Lag time

Lag time implemented in model

Let’s use a 2-compartment model with absorption compartment to illustrate how this can be achieved.

model <- model_suite$nonmem$advan4_trans4

For this example, we’re going to define a lag time ALAG1 for this absorption compartment.

First let’s create a new parameter ALAG1, log-normally distributed with a median of 2 hours and 20% CV.

model <- model %>% add(Theta(name="ALAG1", value=2))
model <- model %>% add(Omega(name="ALAG1", value=20, type="cv%"))

Now, let’s add an equation to the drug model to define ALAG1.

model <- model %>% add(Equation("ALAG1", "THETA_ALAG1*exp(ETA_ALAG1)"))

Finally, we need to tell CAMPSIS that ALAG1 corresponds to a lag time.

model <- model %>% add(LagTime(compartment=1, rhs="ALAG1"))

Our persisted drug model would look like this:

model

## [MAIN]
## KA=THETA_KA*exp(ETA_KA)
## CL=THETA_CL*exp(ETA_CL)
## V2=THETA_V2*exp(ETA_V2)
## V3=THETA_V3*exp(ETA_V3)
## Q=THETA_Q*exp(ETA_Q)
## S2=V2
## ALAG1=THETA_ALAG1*exp(ETA_ALAG1)
## 
## [ODE]
## d/dt(A_DEPOT)=-KA*A_DEPOT
## d/dt(A_CENTRAL)=KA*A_DEPOT + Q*A_PERIPHERAL/V3 + (-CL/V2 - Q/V2)*A_CENTRAL
## d/dt(A_PERIPHERAL)=-Q*A_PERIPHERAL/V3 + Q*A_CENTRAL/V2
## d/dt(A_OUTPUT)=CL*A_CENTRAL/V2
## F=A_CENTRAL/S2
## 
## [LAG]
## A_DEPOT=ALAG1
## 
## [ERROR]
## CONC=F
## CONC_ERR=CONC*(EPS_PROP + 1)
## 
## 
## THETA's:
##    name index value   fix
## 1    KA     1     1 FALSE
## 2    CL     2     5 FALSE
## 3    V2     3    80 FALSE
## 4    V3     4    20 FALSE
## 5     Q     5     4 FALSE
## 6 ALAG1     6     2 FALSE
## OMEGA's:
##    name index index2  value   fix type
## 1    KA     1      1  0.025 FALSE  var
## 2    CL     2      2  0.025 FALSE  var
## 3    V2     3      3  0.025 FALSE  var
## 4    V3     4      4  0.025 FALSE  var
## 5     Q     5      5  0.025 FALSE  var
## 6 ALAG1     6      6 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_DEPOT (CMT=1)
## A_CENTRAL (CMT=2)
## A_PERIPHERAL (CMT=3)
## A_OUTPUT (CMT=4)

Now, let’s now give a simple bolus and simulate with and without ALAG1.

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

results_alag <- model %>% simulate(dataset=ds1, seed=1)
results_no_alag <- model_suite$nonmem$advan4_trans4 %>% simulate(dataset=ds1, seed=1)
gridExtra::grid.arrange(shadedPlot(results_alag, "CONC"), shadedPlot(results_no_alag, "CONC"), nrow=1)

Lag time implemented in dataset

The same simulation can be performed by defining a lag time to the bolus in the dataset.

For this, we need to sample ALAG1 values. This can be done as follows:

distribution <- ParameterDistribution(model=model, theta="ALAG1", omega="ALAG1")
alagValues <- (distribution %>% sample(as.integer(50)))@sampled_values

Then, we can inject them into the dataset.

ds2 <- Dataset(50) %>%
  add(Bolus(time=0, amount=1000, lag=alagValues)) %>%
  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  1.77     1     1  1000     1     0      1
## 2     2     0  2.07     1     1  1000     1     0      1
## 3     3     0  1.69     1     1  1000     1     0      1
## 4     4     0  2.74     1     1  1000     1     0      1
## 5     5     0  2.13     1     1  1000     1     0      1
## 6     6     0  1.70     1     1  1000     1     0      1

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

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