- Computes non-compartmental analysis (NCA) for pharmacokinetic (PK) parameters,
- Generates regulatory compliant listings, tables, and figures,
- Generates word reports,
- Has been validated, against the gold standard NCA software,
- Uses the same area under the curve (AUC) computation method,
- Provides the same interpolation methods.
- Provides flexible handling of the data below the limit of quantification (BLQ),
- Compatible with CDISC SDTM, ADaM, WinNonlin, CSV and Excel data inputs,
- Allows fully script-based and reproducible analyses,
- Allows for flexible and easy stratification.
- NCA are useful to help characterizing the PK properties of drug products,
- Available NCA software often rely on a “point and click” interface and generate multiple output tables across which the important information is scattered,
- IQnca is a free open source R package designed to perform fully reproducible NCA analyses with R. The output data format used by IQnca is intuitive, easy to define, and it contains all the information needed to reproduce an analysis from data to report.
- IQnca is being developed using good coding standards,
- IntiQuan internally validates IQnca and ensures consistency of results against the gold standards,
- The open source approach allows for peer-review and collaborative improvement,
- The distributed open-source version is released without a validation certificate. For a full validation suite check with IntiQuan at email@example.com.
- This webpage currently serves as the main documentation for IQnca.
- The IQnca users group can be accessed here,
- You can join the group as a member if you sign in with your Google account,
- Alternatively you create a post without a login acount by sending an email to firstname.lastname@example.org. Please however note that posts from non-members will be moderated before sending out to the whole group.
Future IQnca Developments
- Adding handling of urine data,
- Develop a shiny-based user interface that brings a “point and click” interface while maintaining reproducibility of the analyses.