Creating Pharmacokinetic Graphs using SAS/GRAPH

Graphs are visual representation of the data. In a clinical trial setting, figures show the general trend and relationships of variables collected during the conduct of a study. In the pharmacokinetic world, graphs play an important role in showing the drug’s concentration profiles. Line graph is a typical way of presenting drug’s concentration. However, depending on the data available and the analysis required, different visual presentations, such as scatter plots, spaghetti plots and many more, are needed to fully communicate the information at hand. These graphs can be achieved by using SAS/GRAPH ® . This paper demonstrates some available SAS/GRAPH ® procedures, options and the Annotate facility for producing effective pharmacokinetic graphs. INTRODUCTION Pharmacokinetics (PK) may be defined as the absorption, distribution, metabolism, and excretion of an investigational drug. In a new drug development process, pharmacokinetic types of studies are conducted to examine what is happening to the drug within the body. These studies involve analyzing drug concentrations in samples collected from the blood, urine, feces and other matrices. Graphs play an important role in interpreting drug concentration-time data as they provide a quick view of the general trend of the drug concentrations over time. A simple illustration of a concentration-time graph is presented below. In general, this graph is very effective to show the mean concentration-time curves for a dataset with enough quantifiable concentrations over time; however not all cases of data received by programmers, biostatisticians and scientists fit into this picture. Every dataset is different, thus, requiring close attention given to the best way of presenting the information, in order that the data may be correctly interpreted. SAS/GRAPH ® is the most common and available SAS tool to produce graphs. This paper highlights some components of SAS/GRAPH which may be used to produce figures used for pharmacokinetic analysis. Posters SAS Global Forum 2012