In this episode I describe inhibitory indirect response PK-PD models. I describe how they work, how to set up the dataset for NONMEM, and how to code the model in NONMEM. This is the third of 4 episodes on different PK-PD models.

Links discussed in the show:

• Indirect response PK-PD model equations
• Example NONMEM code for inhibitory indirect response PK-PD model
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In this episode I describe effect compartment PK-PD models. I describe how they work, a method of creating exploratory plots, how to set up the dataset for NONMEM, and how to code the model in NONMEM. This is the 2nd of 4 episodes on different PK-PD models.

Links discussed in the show:

• Example NONMEM code for effect compartment model
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In this episode I describe direct effect PK-PD models. I describe how they work, a method of creating exploratory plots to identify direct effect models, how to set up the dataset for NONMEM, and how to code the model in NONMEM. This is the first of 4 episodes on different PK-PD models.

Links discussed in the show:

• Example NONMEM code for direct effect model
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In this episode I discuss different methods of presenting pharmacokinetic and pharmacometrics results. I spoke about using PowerPoint and R Markdown derived HTML files. While I prefer HTML files in nearly all cases, I appreciate that sometimes PowerPoint format is required by some companies. I included multiple suggestions for both PowerPoint and HTML files that can help make the information presentation more effective.

Links discussed in the show:

• Floating table of contents in R Markdown HTML output
• How to include CSS in your R Markdown
• Creating tabs in R Markdown
• Code folding in R Markdown
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This episode covers the terminal elimination rate constant calculated using non-compartmental analysis techniques. I reviewed how the rate constant is used to estimate half-life and extrapolate AUC to infinity, reviewed the methods for calculating the terminal rate constant value, and some important points about sample selection. Finally, I gave my reasons that we should ultimately stop talking about this and fretting over the method of calculation. Instead, let’s design better PK experiments with more optimal sampling to capture decline of the drugs we are studying.

Links discussed in the show:

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This episode covered parallelization in modeling analyses. I spoke about across model parallelization and within model parallelization. I recommended some approaches for choosing how many cores to use in parallelization and also discussed different modeling computer systems you can use.

Links discussed in the show:

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Today I covered 2 different topics. First, I spoke about residual error models when modeling. I discussed additive, proportional, and power models. I also reviewed combined models and when they can be helpful. Then I spoke about modeling report submission packages. I explained what I put in those packages, how they are used, and some potential changes coming in the future.

Links discussed in the show:

• Publication on model master file
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This episode discusses simulations. I group simulations into 4 categories: (1) average response, (2) population, (3) individual, and (4) clinical trial simulations. I discuss details of each of these types of simulations and the different variability terms included in each one. I strongly recommend that we implement more clinical trial simulations in our work. This will help us design and execute more successful clinical trials that take advantage of our existing knowledge about a drug. Enjoy the show and connect with me using the links below if you have any questions.

Links discussed in the show:

• You can connect with me on LinkedIn and send me a message
• Send me a message
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