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33: Effect compartment PK-PD models (Ep. 33)

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…

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32: Direct effect PKPD models (Ep. 32)

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…

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31: What should you use to present PK results? (Ep. 31)

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…

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30: How important is terminal rate constant? (Ep. 30)

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…

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29: Parellelization in modeling (Ep. 29)

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: You can connect with me on LinkedIn and send me…

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28: Residual errors and submission packages (Ep. 28)

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…

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27: Simulations (Ep. 27)

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…

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26: Different work environments (Ep. 26)

Today’s show is about different work environments and not scientific topics. Throughout my career I have worked in many different types of companies, and I would like to share some observations with you from those experiences. I have held positions at startup companies or small biotechnology companies, large pharmaceutical companies, contract research laboratories, consulting firms,…

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25: Biosimilar products (Ep. 25)

Today’s show discussed biosimilar development from a clinical pharmacology perspective. I describe what biosimilar products are and the necessary comparisons to the reference product. I review requirements for PK similarity, PD similarity, immunogenicity, and safety. I also touch on the idea of interchangeability between the biosimilar and reference products. Links discussed in the show: FDA…

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24: Choosing an absorption model (Ep. 24)

Today’s show discussed approaches for modeling absorption profiles. I discussed the rationale behind modeling absorption and then described some common models. These included first-order absorption, zero-order absorption, transit compartments, distributed delay, Weibull, and zero/first order absorption. I also provided my recommendations on how to model absorption. Links discussed in the show: Distributed delay modeling in…

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