Repository files navigation

Our aim is to develop and share an open-source R Shiny application for performing Non-Compartmental Analysis (NCA) on clinical and non-clinical datasets worldwide and across pharmaceutical companies.

This application enables users to upload their datasets and perform Non-Compartment Analysis (NCA) on both pre-clinical and clinical datasets, with the results being easily visualizable. Designed with user-friendliness in mind, this app aims to make NCA accessible and straightforward for all scientists. Among the features it currently possess, the App can:

• Customize half life calculation: Either by rule settings definitions or performing manual in-plot adjustments
• Define AUC intervals of interest: Providing by default last and to-infinite calculations
• Visualize data and results with interactive boxplots, summary statistic tables and scatter plots
• Produce PP and ADPP dataset formats of the resulting parameters
• Save your analysis settings and reupload them later to keep on analysing!

The stable version of the package can be easily downloaded:

install.packages("aNCA")

We recommend usingpak for package installation, along with all system dependencies. If you do not havepak available, you will need to set it up first:

install.packages("pak")

then you can installaNCA by running:

pak::pak("pharmaverse/aNCA")

in your R console.

Alternatively, you can set up the package by cloning the repository through your terminal/shell:

git clone https://github.com/pharmaverse/aNCA.git

and then loading it directly usingdevtools in your IDE (e.g. RStudio) console:

devtools::load_all()

To run the application, simply invoke:

aNCA::run_app()

The testing data will be automatically loaded upon application startup. You can provide your own dataset in thedata tab. Here you can also specify pre-processing filters.

In theNCA tab, start off by loading the pre-processed data usingSubmit button. You will also need to choose dose number in theSettings. Then, you will be able to run the NCA analysis. From there, you can also specify different analysis options, like applying flag rule sets and selecting slopes.

After the setup is done and analysis is performed, you are free to explore the results in theOutputs tab. Application supports various customizable plots, as well as report exporting.

For more detailed instructions, check out theGet Started page.

Check ourcontributing guidelines.

• Go toour Website for further information on theaNCA app.
• The main package used by the App isPKNCA. You can find more of it on itsGitHub
• aNCA has different system validations, you can consult themhere