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https://github.com/metrumresearchgroup/mrgsolve
Simulate from ODE-based population PK/PD and QSP models in R
https://github.com/metrumresearchgroup/mrgsolve
mrgsolve ode r rstats
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Simulate from ODE-based population PK/PD and QSP models in R
- Host: GitHub
- URL: https://github.com/metrumresearchgroup/mrgsolve
- Owner: metrumresearchgroup
- Created: 2016-02-12T19:25:20.000Z (almost 9 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T14:33:08.000Z (8 months ago)
- Last Synced: 2024-05-22T14:34:16.615Z (8 months ago)
- Topics: mrgsolve, ode, r, rstats
- Language: R
- Homepage: https://mrgsolve.org
- Size: 35.8 MB
- Stars: 125
- Watchers: 29
- Forks: 34
- Open Issues: 76
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
- jimsghstars - metrumresearchgroup/mrgsolve - Simulate from ODE-based population PK/PD and QSP models in R (R)
README
---
pagetitle: "mrgsolve"
date: ""
author: ""
output: github_document
---
```{r,echo=FALSE}
library(lattice)
library(knitr)
```
```{r, include = FALSE}
knitr::opts_chunk$set(
comment = ".",
fig.path = "man/figures/README-"
)
options(tibble.print_min = 5, tibble.print_max = 5)
```
# mrgsolve 
[](https://github.com/metrumresearchgroup/mrgsolve/actions/workflows/main.yaml)
[](https://cran.r-project.org/package=mrgsolve)
[](http://www.gnu.org/licenses/gpl-2.0.html)
[](https://github.com/metrumresearchgroup/mrgsolve/issues)
[](http://metrumrg.com)
mrgsolve is an R package for simulation from hierarchical, ordinary differential
equation (ODE) based models typically employed in drug development. mrgsolve
is free and open-source software.
## Resources
Please see [mrgsolve.github.io](https://mrgsolve.github.io) for additional
resources, including:
- [User Guide](https://mrgsolve.github.io/user_guide)
- [R Documentation](https://mrgsolve.github.io/docs)
- [Vignettes](https://mrgsolve.github.io/vignettes)
- [Gallery](https://github.com/mrgsolve/gallery)
## Installation
Install the latest release on CRAN
```{r,eval=FALSE}
install.packages("mrgsolve")
```
__Please__ be sure to see important install-related information
[here](https://github.com/metrumresearchgroup/mrgsolve/wiki/mrgsolve-Installation).
Install the current development version
```{r,eval=FALSE}
remotes::install_github("metrumresearchgroup/mrgsolve")
```
## Interaction
We welcome __questions__ about anything mrgsolve: installation, getting your
model to work, understanding better how mrgsolve works. We also welcome __suggestions__
for how to make mrgsolve more useful to you and to the pharmacometrics community.
Please interact with us at the [Issue Tracker](https://github.com/metrumresearchgroup/mrgsolve/issues).
This requires a GitHub account.
## Some examples
### A simple simulation
```{r,echo=FALSE}
options(mrgsolve_mread_quiet = TRUE)
```
```{r,message=FALSE}
library(mrgsolve)
```
Load a model from the internal library
```{r}
mod <- mread("pk1", modlib())
```
Simulate a simple regimen
```{r,pk-figure}
mod %>%
ev(amt = 100, ii = 24, addl = 9) %>%
mrgsim(end = 300, delta = 0.1) %>%
plot(CP~time)
```
A more complicated regimen: 100 mg infusions over 2 hours every 24 hours
for one week, followed by 50 mg boluses every 12 hours for 10 days:
```{r,regimen-figure}
mod %>%
ev_rx("100 over 2h q 24 x 7 then 50 q 12 x 20") %>%
mrgsim(end = 600, delta = 0.1) %>%
plot(CP~time)
```
### Population simulation
```{r}
mod <- mread("popex", modlib()) %>% zero_re()
```
A data set looking at different patient weights and doses
```{r, message=FALSE}
library(dplyr)
data <- expand.ev(amt = c(100,150), WT = seq(40,140,20)) %>% mutate(dose = amt)
head(data)
```
Simulate
```{r, population-figure}
mod %>%
data_set(data) %>%
carry_out(dose,WT) %>%
mrgsim(delta = 0.1, end = 72) %>%
plot(IPRED~time|factor(dose),scales = "same")
```
### Sensitivity analysis with PBPK model
```{r}
mod <- modlib("pbpk")
```
Reference
```{r, comment = " ", echo=FALSE}
blocks(mod,PROB)
```
Model parameters
```{r}
param(mod)
```
Set up a batch to simulate
```{r}
idata <- expand.idata(Kpli = seq(4,20,2))
idata
```
```{r, pbpk-figure}
mod %>%
ev(amt = 150) %>%
idata_set(idata) %>%
mrgsim(end = 6, delta = 0.1) %>%
plot(Cp~time)
```