The IDDI team is excited to present a scientific poster highlighting how R can provide a flexible and cost-effective environment for Pharmacokinetic Noncompartmental Analysis (PK NCA) while reducing software transitions and supporting tailored QC workflows.
In the spirit of entrepreneurship and innovation, many CROs and individual scientists seek low-cost, flexible solutions for conducting pharmacokinetic noncompartmental analyses (PK NCA). Traditional industry-standard software, while widely used, can be expensive and limited in configurability. The growing adoption of R, within the biotechnology and pharmaceutical communities, has created new opportunities for more adaptable and cost-effective PK workflows. In this poster, we describe how we developed and customized an R-based approach for generating PK parameters, initially for QC purposes and for comparison with established tools. We also highlight how R offers efficiency, versatility, visually appealing tables, listings, and figures (TLFs), and a unified workflow without the need for multiple software programs.
This poster was presented at the PHUSE US Connect 2026 by:
- Jennifer Menda, Statistical Programmer – PK Analyst, IDDI
- Haley Evans, Biostatistician, IDDI
Learn more about IDDI’s uncompromising excellence in pharmacokinetics and pharmacodynamics (PK/PD) services that are grounded in biostatistics expertise.
Abstract:
Pharmacokinetic Noncompartmental Analysis (PK NCA) is often performed using extensive, proprietary software with limited flexibility. This poster presents R as a powerful, cost-effective alternative for conducting PK NCA, data manipulation, and visualization. We developed an R script that reads ADPC-like files, calculates PK parameters using packages such as pkNCA and pkr, and generates outputs comparable to conventional tools. Results from multiple studies and administration routes (e.g., extravascular, IV infusion, and IV bolus) confirm consistency and highlight procedural differences. Our workflow enables complete analysis within R, including dataset preparation, parameter calculation, and generation of tables and figures. This approach reduces software switching, streamline processes, and supports reproducibility – making PK NCAs more accessible and efficient for teams of all sizes.
