library(mrgsolve)
library(dplyr)
options(mrgsolve.soloc="build")

1 Introduction

Event objects are simple ways to implement PK dosing events into your model simulation.

2 Setup

Let’s illustrate event objects with a one-compartment, PK model. We read this model from the mrgsolve internal model library.

mod <- mread_cache("pk1cmt", modlib(), end=216, delta=0.1)

3 Events

Events are constructed with the ev function

e <- ev(amt=100, ii=24, addl=6)

This will implement 100 unit doses every 24 hours for a total of 7 doses. e has class ev, but really it is just a data frame

e
. Events:
.   time amt ii addl cmt evid
. 1    0 100 24    6   1    1
as.data.frame(e)
.   time amt ii addl cmt evid
. 1    0 100 24    6   1    1

We can implement this series of doses by passing e in as the events argument to mrgsim

mod %>% mrgsim(events=e) %>% plot(EV1+CP~time)

The events can also be implemented with the ev constructor along the simulation pipeline

mod %>%
  ev(amt=100, ii=24, addl=6) %>%
  mrgsim %>% 
  plot(CP~time)

4 Event expectations

  1. amt is required
  2. evid=0 is forbidden
  3. Default time is 0
  4. Default evid is 1
  5. Default cmt is 1

Also by default, rate, ss and ii are 0.

5 Combine events

mrgsolve has operators defined that allow you to combine events. Let’s first define some event objects.

e1 <- ev(amt=500)
e2 <- ev(amt=250, ii=24, addl=4)
e3 <- ev(amt=500, ii=24, addl=0)
e4 <- ev(amt=250, ii=24, addl=4, time=24)

We can combine e1 and e3 with a collection operator

c(e1,e4)
. Events:
.   time amt cmt evid ii addl
. 1    0 500   1    1  0    0
. 2   24 250   1    1 24    4

mrgsolve also defines a %then$ operator that lets you execute one event and %then% a second event

e3 %then% e2
. Events:
.   time amt ii addl cmt evid
. 1    0 500 24    0   1    1
. 2   24 250 24    4   1    1

Notice that e3 has both ii and addl defined. This is required for mrgsolve to know when to start e2.

6 Combine event objects to create a data set

We can take several event objects and combine them into a single simulation data frame with the as_data_set function.

e1 <- ev(amt=100, ii=24, addl=6, ID=1:5)
e2 <- ev(amt=50,  ii=12, addl=13, ID=1:3)
e3 <- ev(amt=200, ii=24, addl=2, ID=1:2)

When combined into a data set, we get * N=5 IDs receiving 100 mg Q24h x7 * N=3 IDs receiving 50 mg Q12h x 14 * N=2 IDs receiving 200 mg Q48h x 3

data <- as_data_set(e1,e2,e3)
data
.    ID time cmt evid amt ii addl
. 1   1    0   1    1 100 24    6
. 2   2    0   1    1 100 24    6
. 3   3    0   1    1 100 24    6
. 4   4    0   1    1 100 24    6
. 5   5    0   1    1 100 24    6
. 6   6    0   1    1  50 12   13
. 7   7    0   1    1  50 12   13
. 8   8    0   1    1  50 12   13
. 9   9    0   1    1 200 24    2
. 10 10    0   1    1 200 24    2

To simulate from this data set, we use the data_set function. First, let’s load a population PK model

mod <- mread_cache("popex", modlib())
mod %>% data_set(data) %>% mrgsim(end=336) %>% plot(GUT+DV ~  .)

mrgsolve: mrgsolve.github.io | metrum research group: metrumrg.com