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https://github.com/insightrx/mipdtrial

Tools for simulating model-informed precision dosing trials
https://github.com/insightrx/mipdtrial

mipd pharmacokinetics pharmacometrics

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Tools for simulating model-informed precision dosing trials

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README 

          
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# mipdtrial



The goal of mipdtrial is to make it easy to simulate pharmacokinetic/

pharmacodynamic (PK/PD) endpoints in response to dose adaptation.



Existing tools are cumbersome to use for this purpose. For example, tools like

NONMEM are optimized for model development, and assume fixed regimens when used

for simulation. Other algorithms, like sample optimization simulations, optimize

for information gain and not for attainment of clinically relevant metrics, such

as AUC target attainment. `mipdtrial` fills in this niche by helping users 

simulate PK/PD resulting from dose adaptations informed by past PK/PD readouts.



Here are some example sorts of questions:



* Will fewer patients receive a therapeutic AUC if we change our clinical 

protocol from collecting a peak and a trough sample to collecting a single 

mid-interval sample?

* How might model misspecification impact patient target attainment?

* How does my institution's nomogram compare to a model-based dose?



## Installation



You can install the development version of mipdtrial from [GitHub](https://github.com/) with:



```r

# install.packages("devtools")

devtools::install_github("InsightRX/mipdtrial")

```

For examples of how to use the package to answer questions about MIPD and

target attainment, check out the vignettes listed under "Articles!"



## Usage



To use the `mipdtrial` package, it is crucial to understand the concept of "design" 

that is introduced in the package. The main goal of the designs is to allow 

configuration of flexible trials in which the sampling and regimen updates can 

depend on prior regimen changes, such as when the dosing interval is changed, 

but we still want to sample at dose 7 regardless of the dosing interval. Or, 

when infusion lengths are changed, but we still want to sample a peak sample. 

When times are pre-specified and fixed this flexibility is not possible. 



The following designs need to be configured for every trial simulation:



- `sampling_design` : determines at what timepoints samples are taken.

- `target_design`: determines at what timepoint the target should be measured, 

and what the target is.

- `regimen_update_design` : determines at what timepoint the dose can be updated 

in response to new information sampled using the `sampling_design`, and how

to optimize the dosing regimen.



All of these three designs can be "anchored" to a specific dose or day number. 

They can also be offset from the dosing time to e.g. sample at "peak" or "trough" 

times. Here is an example of how to set up a design for a simulated MIPD trial:



```r

## sample at peak (at 1-hour infusion end), and at true trough

## do this at dose #1 and #3

tdm_design <- create_sampling_design(

  when = c("peak", "trough", "peak", "trough"),

  at = c(1, 1, 3, 3),

  anchor = "dose"

)



## Now sample slightly more realistically, half an hour after infusion end,

## and half an hour before true trough. We can use `offset` for this:

tdm_design <- create_sampling_design(

  when = c("peak", "trough", "peak", "trough"),

  offset = c(0.5, -0.5, 0.5, -0.5), 

  at = c(1, 1, 3, 3),

  anchor = "dose"

)



## If you know the sampling times and dosing intervals are not going 

## to change during the trial, you could also specify this design simply

## using fixed times as: (assuming 12-hour intervals)

tdm_design <- create_sampling_design(

	time = c(1.5, 11.5, 25.5, 35.5)

)



## For targets, we follow broadly the same concept. To target an AUC4 of 

## 400-600 at day 6, we can write:

target_design <- create_target_design(

  targettype = "auc24", 

  targetmin = 400,

  targetmax = 600,

  at = 6,

  anchor = "day"

)



## And for regimen update designs, it works similar as well. The following code

## implements dose updates at dose #3 and #5, using a MAP-based optimization.

dose_update_design <- create_regimen_update_design(

  at = c(3, 5),

  anchor = "dose",

  update_type = "dose",

  dose_optimization_method = map_adjust_dose

)

```



The vignettes show various additional examples of how to set up the trial 

simulations using designs. If you find an example of an MIPD trial design 

that cannot be captured yet using these functions, please let us know.



## Roadmap



The `mipdtrial` package is currently under development, and there will likely be 

changes to core functionality in the upcoming months. The following features are

on our short-term roadmap:



- Implement functionality to gather all relevant information during and at the 

end of the simulated trial, and include it in the output from `sample_and_adjust_by_dose`.

- Improve ease of use: implement a single function to run the trial

- Add more optimization functions, e.g. combined dose- and interval- optimization

- Add functionality to generate more realistic trial scenarios, e.g. allow some

random scattering of TDM samples over time.



## Contributing



We welcome input from the community:



- If you think you have encountered a bug, please [submit an issue](https://github.com/InsightRX/mipdtrial/issues) on the GitHub 

page. Please include a reproducible example of the unexpected behavior.



- Please [open a pull request](https://github.com/InsightRX/mipdtrial/pulls) if you have a fix or updates that would improve the package. If you're not sure if your proposed 

changes are useful or within  scope of the package, feel free to contact one 

of the authors of this package.



## Disclaimer



The functionality in this R package is provided "as is". While its authors 

adhere to software development best practices, the software may still contain 

unintended errors.



InsightRX Inc. and the authors of this package can not be held liable for any

damages resulting from any use of this software. By the use of this software 

package, the user waives all warranties, expressed or implied, including any 

warranties to the accuracy, quality or suitability of InsightRX for any 

particular purpose, either medical or non-medical.



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