sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis

Area of Science:

• * Pharmacology and Toxicology
• * Data Science and Bioinformatics

Background:

• * The CDISC Standard for Exchange of Nonclinical Data (SEND) enables data standardization in toxicology.
• * Facilitating cross-study analyses of SEND datasets is crucial for drug development.
• * Open-source software solutions are needed to leverage SEND data effectively.

Purpose of the Study:

• * To develop and publicize novel methods for cross-study analysis of SEND datasets.
• * To create an open-source R package, sendigR, for constructing relational databases from SEND datasets.
• * To enable harmonization of terminology within SEND datasets using controlled terminologies.

Main Methods:

• * Development of the R package `sendigR` in collaboration with the Pharmaceutical Users Software Exchange (PHUSE).
• * Integration of the Python package `xptcleaner` for terminology harmonization.
• * Inclusion of an R Shiny web application for historical control analyses without coding experience.

Main Results:

• * The `sendigR` R package allows users to build a relational database from SEND datasets.
• * Users can query the database to perform cross-study analyses.
• * Terminology harmonization is achieved by mapping to CDISC controlled terminologies via `xptcleaner`.
• * An R Shiny application is available for non-programmers to conduct historical control analyses.

Conclusions:

• * `sendigR` provides a valuable open-source tool for researchers analyzing toxicology data.
• * The package democratizes cross-study analysis of SEND data for both experienced programmers and toxicologists.
• * `sendigR` is freely available on CRAN and GitHub, promoting collaborative development and accessibility.