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Scrambled Data – A Population PK/PD Programming Solution


Cytel participated at PharmaSUG 2016 in Denver recently.  A key event on the statistical programming global calendar, the topics included Submission Standards, Application Development and Data Visualizaton. Sharmeen Reza, Associate Director Statistical Programming at Cytel was selected to present a paper presentation in the Statistics and Pharmacokinetics stream on the topic of Scrambled Data- A Population PK/ PD Programming Solution.  The presentation was very popular and well attended, so we've included an abstract of the presentation  below, and made her slides available for download. 

Population PK/ PD Modeling and Simulation

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) modeling and simulation is a necessity in the drug discovery process. It allows PK scientists to evaluate and present safety and potency of a drug. Regulatory agencies require population analysis results as part of submission package. Scientists' involvement in mainstream clinical study team is essential in aligning analysis timelines with study conduct activities. In order to support analyses, pop-PK/PD programmers create NONMEM®-ready data sets at different stages of a trial. It is critical to deliver data sets to PK scientists in a timely manner enabling them to prepare models, and optimize based on updated data at each stage. Upon receiving final data, pop-PK/PD programmers produce NONMEM-ready data set in a short window after a study database lock.

Sensitivity of data

Due to the sensitivity of PK data, accessibility is a major difficulty that programmers face during the development phase. Since blank concentration results is not a feasible option for data set creation and in turn PK analyses, a reasonable solution is to build and test code on scrambled data at intermediate stages. At present, formal data requests need to be in place and takes several weeks to process. The idea is to have scrambled data available throughout a trial with pre-planning and required approval as necessary. Careful measure needs to be taken for scrambling PK related variables where sample collection method is not standardized and regular randomization process is not in effect. Suitable SAS® techniques are discussed in this paper with clear advantages of scrambling in research and development.

Download Sharmeen's slides below. 



To learn more about Cytel's services in Quantitative Pharmacology and Pharmacometrics click below:





Further Reading

Blog :It's time to bridge the gap between pharmacometrics and biostats

Blog: Quantitative Pharmacology for Biomarker Development


With thanks to Sharmeen Reza, Associate Director Statistical Programming

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