Over the course of my career, I have taught the theory and practical applications of PK/PD modeling to hundreds of scientists. In this blog post, I’ll share some of my most popular tips for solving common difficulties encountered by pharmacometricians.
The tools of the trade
While I have worked with a number of pharmacometrics tools, I find that the Certara’s Phoenix platform, including Phoenix WinNonlin, Phoenix NLME for population PK/PD modeling, and Phoenix Connect, strikes a good balance between simple to use and robust enough for complex model development. In particular, I enjoy the integrated Phoenix interface that supports both non-compartmental analysis and complex population PK/PD modeling.
Data preparation tips
As most of you probably already know, getting your data ready for modeling can take significant amounts of time. Here are some suggestions for ways to manipulate your data efficiently and get on to the real business of model development.
- Changing column names and units in Phoenix: One of the most common tasks when working with data in Phoenix is to change the column titles or units. In many software packages that consists of clicking on the data spreadsheet and re-typing the new information; however, with Phoenix, you have to take a few additional steps. Read more
- How to filter data with Phoenix WinNonlin: You can filter data using Phoenix’s WinNonlin’s powerful Data Wizard object. For this example, let’s assume we have completed a non-compartmental analysis, and now we want to create a subset of results that includes just a few of the PK parameters (Cmax, Tmax, and AUCall). This is commonly done when completing an analysis to support a bioequivalence study. The Cmax and AUC are needed for statistical analysis, but all other PK parameters are only presented in a summary table. By creating a standard filter, you can quickly add this filter object to any workflow where you need to subset your data. Read more
Tricks to help you increase your efficiency
- Adding a placebo component to your PK/PD model in Phoenix: Most often, we try to model the effect of a drug by drawing relationships between the concentration and effect. This usually entails subsetting the data to exclude information from subjects that received placebo during the trial. But statistical comparisons in clinical studies are most often performed by comparing active treatment to placebo treatment. This is because we have to correct for disease progression and the psychological effects of the placebo treatment. Include placebo treatments in your PK/PD models to make them more useful for simulations and collaboration with other scientists. Read more
- Creating templates in Phoenix WinNonlin: One of the most useful features of the Phoenix software is the ability to re-use objects for new projects. In particular, it takes quite a bit of time to adjust all of the settings for figures in Phoenix. You need to adjust the font on both axes, change the legend labels, choose the appropriate symbols and lines for the data, and create a title. Then, you need to create a similar figure for another study. Wouldn’t it be great to take the existing figure that you created and then paste that into the new project? Read more
Pharmacometrics theory, explained
- How does Monte Carlo simulation work? The term “Monte Carlo simulation” is often used in the modeling and simulation literature on PK/PD analysis. When I was first exposed to this term, I was thoroughly confused and thought that it was some exotic statistical method that required 3 PhDs and a few days to comprehend. But, it is a simple concept that everyone can grasp. Read more
- How to derive the logarithmic trapezoidal AUC calculation: Calculating the area under the curve can be accomplished with one of two methods: the first follows the “linear trapezoidal rule” and the second follows the “logarithmic trapezoidal rule”. These equations are normally presented in textbooks without derivations, so all you have to do is insert the concentrations and times, and you can calculate the area under the curve. I want to show you how those equations are developed so that you understand what you are doing when you calculate the AUC. Read more
- How to extrapolate AUC to infinity: The AUC is a pharmacokinetic statistic used to describe the total exposure to a drug. More specifically, it is the time-averaged concentration of drug circulating in the body fluid analyzed (normally plasma, blood or serum). Standard calculation of AUC involves using non-compartmental techniques to calculate the AUC from time 0 to the last measurable concentration. This is called AUC0-t and represents the observed exposure to a drug. But what happens after the last measurable concentration? How much drug is “left” in the body? And what happens to it? Read more
- Bioanalysis from a PK perspective: The field of bioanalytical chemistry, or bioanalysis, is an important area of research that has a direct impact on the work of pharmacokineticists. Essentially, bioanalysis converts a blood sample (or any other matrix) into a drug concentration by the use of analytical equipment. There are a variety of topics related to bioanalysis that should be of interest to individuals in the field of pharmacokinetics. After all, bioanalysis results are the starting materials for pharmacokinetic analysis. Just as any good cook wants to use the best ingredients to make a delicious meal, so too must the pharmacokineticist select the best bioanalysis methodologies and tools to ensure the highest quality pharmacokinetic analysis. Read more
- Simplifying deconvolution: Deconvolution is used to evaluate the absorption kinetics of a drug. Unfortunately, the term can be confusing and explanations are generally even more confusing. While deconvolution is not a simple topic, I believe it can be understood so that more scientists can apply the principles to their work. Read more
My colleague, Colin Chang, recently gave a webinar where he demonstrated how Certara scientists used the population PK modeling tool, Phoenix NLME, to optimize dosing of a weight loss drug in patients with hepatic impairment. I hope that you’ll view the recording. Let me know what you think in the comments section!