In this episode I discuss dataset-JSON, a new proposed data standard for CDISC files and explain some of the CDISC files I use in my clinical pharmacology work. I end with some brief thoughts about the ACoP 2025 meeting in Denver in October 2025.

Links discussed in the show:

• CDISC dataset-JSON
• CDISC files I use
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In this episode I discuss review statistics that are commonly used in pharmacokinetics and pharmacometrics work. I reviewed the basic theory of the normal distribution and then discussed various individual statistics that we use in PK work. I concluded with some examples of which statistics I use in my work.

Links discussed in the show:

• Link to message about harmonic mean

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In this episode I discuss clinical trial simulations. I review concepts of trial simulation including different variability terms and when to use them. I also share my thoughts on 3 different applications used in clinical trial simulation.

Links discussed in the show:

• Trial Simulator Software page
• Simulx Software page
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In this episode I discuss R Shiny and how it can be used for building clinical pharmacology tools. I provide an overview of the technology, suggest a few example use cases, and then walk through a specific practical example of predicting AUC and Cmax for future doses from observed data. I end with a discussion of the benefits and challenges of using R Shiny for clinical pharmacology tools.

Links discussed in the show:

• Basics about R Shiny
• Shinyapps.io for hosting shiny apps
• Example R Shiny app by Samer Mouskassi: ggplot with your data
• Example R Shiny app for AUC-Cmax predictions
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In this episode I discuss noncompartmental analysis. I categorize PK parameters calculated by NCA methods as either Important, Useful, or Questionable. I also share my thoughts on how to report PK parameters calculated using NCA methods in nonclinical and clinical reports. I want to hear your thoughts about this episode. Do you agree or disagree with my categorization of PK parameters? Why? Use the links below to let me know.

Links discussed in the show:

• You can connect with me on LinkedIn and send me a message
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In this episode I review the process I follow for fitting a base pharmacokinetic (PK) model. I talk from the perspective of an individual PK model, but include some differences associated with population PK models. I go over exploratory data analysis, getting initial estimates, and how to choose between different base models.

Links discussed in the show:

• AIC and BIC
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In this episode I discuss different algorithms used for pharmacometrics modeling. I describe difference between maximum likelihood and expectation maximization methods. I review the FO and FOCEI maximum likelihood algorithms. I then review SAEM, IMP, and QRPEM expectation maximization algorithms that are available. I conclude with an brief explanation of the difference between parameter estimation and parameter uncertainty.

Links discussed in the show:

• PMXRepo
• James Ousey LinkedIn page
• Manuscript by Liu and Wang, 2016
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In this episode I discuss my experience as an independent consultant in the clinical pharmacology and pharmacometrics space. I talk about how I got from high school to become a consultant. I touched on the many different activities I do as an independent consultant. I shared a bit about how I handle compensation with clients and how I pay myself. And I ended with some brief comments about getting customers.

Links discussed in the show:

• Time management with Clockify
• Accounting and invoices with Wave
• Project management with Jira
• Project notes with Confluence
• Computing resources with AWS
• Microsoft 365 for email, sharepoint, and more as a small business
• Profit First
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In this episode I discuss using nlmixr for population PK modeling in R. I give an overview of the software package, describe the installation process, and walk through a simple example. Overall, nlmixr is a nice package that can be used for most modeling activities you encounter. It eliminates the need for a license since it is open-source. There are some challenges associated with using R for modeling with large datasets and long-running models. But once those are overcome, this could be a great package for all-around use.

Links discussed in the show:

• nlmixr website
• nlmixr on github
• Example R code – remove the .txt file type after download
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In this episode I discuss power calculations using the R package PowerTOST. I gave an introduction to power calculations and the statistical premise. I reviewed bioequivalence study designs that are commonly used for generic drug development, food effect evaluation, and drug-drug interaction studies.

Links discussed in the show:

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