This episode is an interview with Michael Stackhouse, the Chief Innovation Officer at Atorus Research. We discussed a variety of topics such as modern data science tools, why regulatory agencies want to get rid of SAS transport files in favor of Dataset JSON file formats, and how AI is being used in data programming and data science.

Links discussed in the show:

• Connect with Mike on LinkedIn
• Learn more about Atorus Research on their website
• You can connect with Nathan on ⁠LinkedIn and send me a message⁠⁠
• Send Nathan a message⁠⁠
• Sign up for Nathan’s newsletter⁠

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This episode is an interview with Dr. Wouter Vaes, the Chief Science Officer of Peregrion. We discuss how accelerator mass spectrometry is being used in early clinical development for drug metabolism and pharmacokinetics. We discuss using this technology to support development of oncology agents, determining absolute bioavailability without IV toxicology work, and more.

Links discussed in the show:

• You can contact Wouter by email: [email protected]
• Connect with Peregrion on LinkedIn or their website
• Symposium on microtracer studies
• FDA guidance on safety testing of drug metabolites
• White paper on human ADME data
• You can connect with Nathan on LinkedIn and send me a message
• Send Nathan a message
• Sign up for Nathan’s newsletter

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This episode is an interview with Aruna Dhontabhaktuni, Founder and CEO of RegKey, a regulatory intelligence platform that uses AI. The discussion touches on how companies can leverage large language model AI tools to accelerate regulatory strategy work.

Links discussed in the show:

• You can contact Aruna by email: [email protected]
• Visit the RegKey website
• Connect with Aruna on LinkedIn
• You can connect with Nathan on LinkedIn and send me a message
• Send Nathan a message
• Sign up for Nathan’s newsletter

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This episode is a collection of my thoughts on model-informed drug development and where different modeling modalities fit into the development schedule. These are just my opinions and each development program is different.

Links discussed in the show:

• You can connect with me on LinkedIn and send me a message
• Send me a message
• Sign up for my newsletter

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In this episode I chat with Ahmed Elmokadem about QSP modeling. Ahmed, from Metrum Research Group, is an expert consultant in QSP modeling and how it can be used to provide a framework to connect mechanistic understanding with clinical pharmacology and decision-making.

Links discussed in the show:

• Connect with Ahmed on LinkedIn on on his website
• Ahmed’s book: Numbers that Dazzled the World
• You can connect with me on ⁠LinkedIn and send me a message⁠⁠
• Send me a message⁠⁠
• Sign up for my newsletter⁠

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
• You can connect with me on LinkedIn and send me a message
• Send me a message
• Sign up for my newsletter

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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

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

• Send me a message

• Sign up for my newsletter

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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
• You can connect with me on LinkedIn and send me a message
• Send me a message
• Sign up for my newsletter

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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
• You can connect with me on LinkedIn and send me a message
• Send me a message
• Sign up for my newsletter

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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
• Send me a message
• Sign up for my newsletter

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