In nlmixr2/rxode2: Facilities for Simulating from ODE-Based Models

Using prior data for solving

rxode2 can use a single subject or multiple subjects with a single event table to solve ODEs. Additionally, rxode2 can use an arbitrary data frame with individualized events. For example when using nlmixr, you could use the theo_sd data frame

library(rxode2)
library(nlmixr2data)

## Load data from nlmixr
d <- theo_sd

## Create rxode2 model
theo <- function() {
  ini({
    tka <- 0.45 # Log Ka
    tcl <- 1 # Log Cl
    tv <- 3.45    # Log V
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
  })
  model({
    ka <- exp(tka + eta.ka)
    cl <- exp(tcl + eta.cl)
    v <- exp(tv + eta.v)
    d/dt(depot) = -ka * depot
    d/dt(center) = ka * depot - cl / v * center
    cp = center / v
  }) 
}

## Create parameter dataset
library(dplyr)
parsDf <- tribble(
  ~ eta.ka, ~ eta.cl, ~ eta.v, 
  0.105, -0.487, -0.080,
  0.221, 0.144, 0.021,
  0.368, 0.031, 0.058,
 -0.277, -0.015, -0.007,
 -0.046, -0.155, -0.142,
 -0.382, 0.367, 0.203,
 -0.791, 0.160, 0.047,
 -0.181, 0.168, 0.096,
  1.420, 0.042, 0.012,
 -0.738, -0.391, -0.170,
  0.790, 0.281, 0.146,
 -0.527, -0.126, -0.198) %>%
    mutate(tka = 0.451, tcl = 1.017, tv = 3.449)

## Now solve the dataset
solveData <- rxSolve(theo, parsDf, d)

plot(solveData, cp)

print(solveData)

## Of course the fasest way to solve if you don't care about the rxode2 extra parameters is

solveData <- rxSolve(theo, parsDf, d, returnType="data.frame")

## solved data
dplyr::as_tibble(solveData)

data.table::data.table(solveData)