Om 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.
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When to use model-informed drug development (Ep. 52) (00:28:16)
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 Copyright Teuscher Solutions LLCAll Rights Reserved
QSP Modeling with Ahmed Elmokadem (Ep. 51) (00:44:37)
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 websiteAhmed’s book: Numbers that Dazzled the World You can connect with me on LinkedIn and send me a messageSend me a messageSign up for my newsletter
CDISC datasets and ACoP 2025 (Ep. 50) (00:18:46)
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 Copyright Teuscher Solutions LLCAll Rights Reserved
Statistics in Pharmacokinetics (Ep. 49) (00:25:13)
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 Copyright Teuscher Solutions LLCAll Rights Reserved
Clinical Trial Simulations (Ep. 48) (00:21:30)
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
Using R Shiny for Clinical Pharmacology (Ep. 47) (00:19:37)
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
What is useful about noncompartmental analysis? (Ep. 46) (00:27:10)
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
How to select a base PK model (Ep. 45) (00:27:40)
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
Different estimation methods used in modeling (Ep. 44) (00:25:37)
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
What is consulting life like? (Ep. 43) (00:30:05)
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 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
Population PK modeling with nlmixr (Ep. 42) (00:25:55)
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 You can connect with me on LinkedIn and send me a message Send me a message Sign up for my newsletterCopyright Teuscher Solutions LLCAll Rights Reserved
Sample size calculations (Ep. 41) (00:34:39)
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:
PowerTOST R package
PowerTOST instructions (scroll down to the Read Me information)
Example R code (Change the filetype to .R after downloading)
Statistical Power
Statistical approaches to establishing Bioequivalence from FDA
Calculation of CV
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Collaboration with BioTalk Unzipped (Ep. 40) (00:13:53)
In this episode I share a couple short clips from my conversation with Chad Briscoe and Greg Austin from BioTalk Unzipped (www.biotalkunzipped.com). We chat about a variety of topics including artificial intelligence and challenges with cell therapy development. You can hear the entire conversation on the BioTalk Unizipped podcast or YouTube channel in January. For now, enjoy these clips from the upcoming show!
Links discussed in the show:
BioTalk Unzipped main page
BioTalk Unzipped YouTube channel
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Toxicokinetics (Ep. 39) (00:19:03)
In this episode I discuss toxicokinetic analysis. This is the analysis of exposure data to support animal toxicology studies. I review the purpose of toxicology studies and how exposure metrics are used. Then I discuss different blood sampling schemes, and how to calculate exposure parameters for both full profiles and mean profiles.
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Steady State (Ep. 38) (00:16:27)
This episode is a discussion of steady state pharmacokinetics. Steady state occurs when the amount of drug input is equivalent to the amount of drug elimination in a dosing interval. I discuss some key PK parameters at steady state and how to use them. I also discuss different methods of confirming that you have reached steady state.
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Dose Proportionality (Ep. 37) (00:16:58)
In this episode I talk about dose proportionality, which is a surrogate for determining if we have linear clearance across a range of dose levels. I discuss 3 different methods that people use to determine dose proportionality along with a recommendation to use the power model method.
Links discussed in the show:
Power model method
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Covariate Search Methods (Ep. 36) (00:27:22)
This episode is a discussion of covariate search methods. I give definitions of covariates and predictors, and then I describe 3 different covariate search methods. For each method I describe the pros and cons associated with each one, including bias and time limitations.
Links discussed in the show:
Evaluation of the Boruta method for covariate selection
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In this episode I describe stimulatory 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 last of 4 episodes on different PK-PD models.
Links discussed in the show:
Indirect response PK-PD model equations
Example NONMEM code for stimulatory indirect response PK-PD model
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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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Direct effect PKPD models (Ep. 32) (00:31:34)
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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What should you use to present PK results? (Ep. 31) (00:31:24)
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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How important is terminal rate constant? (Ep. 30) (00:26:05)
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.
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Parellelization in modeling (Ep. 29) (00:18:51)
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.
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Residual errors and submission packages (Ep. 28) (00:27:40)
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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