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 Copyright Teuscher Solutions LLCAll Rights Reserved
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21:30
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21:30
Using R Shiny for Clinical Pharmacology (Ep. 47)
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 ShinyShinyapps.io for hosting shiny apps Example R Shiny app by Samer Mouskassi: ggplot with your dataExample 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 Copyright Teuscher Solutions LLCAll Rights Reserved
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19:37
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19:37
What is useful about noncompartmental analysis? (Ep. 46)
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 Copyright Teuscher Solutions LLCAll Rights Reserved
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27:10
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27:10
How to select a base PK model (Ep. 45)
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 BICYou can connect with me on LinkedIn and send me a message Send me a message Sign up for my newsletter Copyright Teuscher Solutions LLCAll Rights Reserved
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27:40
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27:40
Different estimation methods used in modeling (Ep. 44)
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:PMXRepoJames Ousey LinkedIn pageManuscript by Liu and Wang, 2016You can connect with me on LinkedIn and send me a messageSend me a messageSign up for my newsletterCopyright Teuscher Solutions LLCAll Rights Reserved
À propos de Clinical Pharmacology Podcast with Nathan Teuscher
I discuss clinical pharmacology and pharmacometrics topics from the perspective of drug development scientists. I share my expertise and knowledge about designing and conducting clinical pharmacology studies and discuss how to analyze the data using the most effective approaches. I draw from my experience of over 20 years working in drug development organizations and consultancies.
France