44 avsnitt • Längd: 25 min • Månadsvis
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.
The podcast Clinical Pharmacology Podcast with Nathan Teuscher is created by Nathan Teuscher. The podcast and the artwork on this page are embedded on this page using the public podcast feed (RSS).
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.
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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.
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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.
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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.
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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!
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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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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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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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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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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.
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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 will help us design and execute more successful clinical trials that take advantage of our existing knowledge about a drug. Enjoy the show and connect with me using the links below if you have any questions.
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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, and working for myself as a consultant, and now as a software developer. Although my work has been focused on clinical pharmacology and pharmacometrics, the jobs and work environments are very different.
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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.
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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.
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Today’s show was an overview of Aplos Analytics and the development story for Aplos NCA. Please consider participating in the Early Access Program. Use the link below to learn more about Aplos NCA.
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Today’s show is about hepatic impairment clinical studies. I cover the rationale behind evaluating hepatic impairment, common study designs, and key factors in the analysis and interpretation of the study results. I also share a huge announcement about Aplos NCA, a cloud-based solution for calculating NCA PK parameters. Use the link below to learn more about Aplos NCA.
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Today’s episode was about Project Optimus, the FDA initiative to improve dose selection and optimization for oncology products. Julie Bullock was a guest on this episode and shared her expertise as a former oncology clinical pharmacology reviewer at the FDA and as a current consultant in the pharmaceutical industry.
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Today’s show is about TMDD or target-mediated drug disposition models. I review the scientific and physiologic basis for TMDD models, how to review data and determine if a TMDD model is needed. I review the various modeling approaches along with my recommendations for specific TMDD models. I hope this information helps you understand how to recognize TMDD behaviors and the options for modeling that data.
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Today’s episode is about communication for scientists. I believe that improvements in communication skills can have a greater impact on your career and work than technical skills. Michael Piperno, an expert in communication and leadership skills joins me to discuss communication challenges and solutions for clinical pharmacologists and pharmacometricians.
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You can connect with Michael
You can connect with me
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Today’s episode is about cell therapies, including CAR T-cell theraphy for cancer. I give an overview of cell therapy and the challenges of clinical pharmacology in this new area of research.
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You can connect with me on LinkedIn and send me a message
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Happy New Year! Today’s episode is all about allometric scaling in pharmacokinetic and pharmacodynamics. In particular, I’ll discuss how to use it for scaling from adults to pediatrics and for selection of first in human doses.
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This is the last podcast for 2023. Thank you for listening throughout the year! In today’s episode I talk about computer system validation for pharmacometrics. I review my experience with validation in multiple companies over 20 years and discuss what auditors and FDA inspectors want to see.
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In today’s episode I cover 4 different modeling approaches: QSP, PBPK, PopPK, and PKPD. All of these are methods for analyzing data to predict either drug concentrations, or a response variable. But each method is developed in a different way and has different assumptions and limitations.
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In today’s episode I discuss BLQ handing for both non-compartmental and modeling analysis approaches. I discuss how BLQ samples are defined, methods for analyzing them, and situations when you should use different analysis approaches.
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In today’s episode I covered the different types of drug interactions, followed by features of clinical drug evaluation studies recommended in the guidance. Then I will review recommendations for both stand-alone clinical drug interaction studies, as well as nested drug interaction studies. I’ll finish with recommended approaches for interpreting drug interaction study results and expressing that information in drug labeling.
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In today’s episode I discuss the evaluation of pharmacokinetics in patients with impaired renal function. I include information from both the US FDA and EMA guidance documents linked below. I discuss the determination of when a dedicated renal impairment study is needed, characteristics of the study design and how to analyze and interpret the results.
Links discussed in the show:
• FDA guidance on PK in patients with impaired renal function
• EMA guidance on Evaluation of PK in patients with decreased renal function
• You can connect with me on LinkedIn and send me a message.
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This episode is a discussion on model evaluation and selection. I discussed diagnostic plots using predictions and residuals. I covered approaches for model selection and factors to consider. The focus of this episode is on models that predict continuous endpoints like drug concentration, biomarkers, or other continuous endpoints. Several figures are mentioned in the podcast and links to those images can be found below.
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This episode is a discussion of the FDA guidance “Exposure-Response Relationships”. The relationship between drug exposure and response is critical to any determination of the safety and effectiveness of a drug product. In this episode I discuss the regulatory applications of exposure-response, the exposure-response relationships over time, the design of studies to collection exposure-response information, and modeling exposure-response relationships. Links discussed in the show: • FDA guidance “Exposure-Response Relationships” • ICH E4 guideline “Dose-Response Information” • You can connect with me on LinkedIn and send me a message • Send me a message • Sign up for my newsletter Copyright Teuscher Solutions LLC All Rights Reserved
This episode I talk about finding a job in Clinical Pharmacology or Pharmacometrics. I share a few thoughts from my experience followed by an interview with TJ Elder, the president of STEM Sourcing Recruiting and Staffing. TJ shares his insights and best practices on securing a new job in Clinical Pharmacology or Pharmacometrics.
Links discussed in the show:
• Connect with TJ Elder on LinkedIn
• You can connect with me on LinkedIn and send me a message
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This episode is a discussion of the FDA and ICH guidance E14 titled “Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs”. I start with background on the reason for these evaluations, then move to an overview of the key evaluations needed. I then discuss in some detail the thorough QT study and the concentration QT analysis. Then I provide some parting thoughts from my experiences with these analyses.
Links discussed in the show:
• ICH E14 Guidance (FDA website)
• Scientific white paper on concentration – QTc modeling by Garnett et. al
• You can connect with me on LinkedIn and send me a message ()
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Thank you to everyone who sent me feedback on this podcast. This episode is based on a suggestion from Kushal and Parmesh. In this episode, I discuss a general clinical pharmacology pediatric extrapolation plan. I describe the basics of the plan that is commonly proposed to regulatory bodies. I discuss specific technical approaches for extrapolation of adult pharmacokinetic data to pediatric patients and the extrapolation of adult exposure-response or pharmacodynamic data to pediatrics. And I conclude with a discussion of how to use physiologic-based pharmacokinetic models for pediatric extrapolation.
Links discussed in the show:
• PBPK Models in Pediatric Development
• You can connect with me on LinkedIn and send me a message
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Thank you to everyone who sent me feedback on this podcast. This episode is based on a suggestion from Drew. In this episode, I discuss bioequivalence studies and generic drug development. I will refer to guidance documents from both the European Medicines Agency or EMA and the US Food and Drug Administration or FDA. First, I will start with a discussion of the definition of bioequivalence or BE. Then I will discuss the statistical basis for bioequivalence and how you can leverage that for study design. And I will end with some unique challenges related to bioequivalence. Throughout, I will give practical suggestions based on my experiences in the past with BE studies.
Links discussed in the show:
• EMA guidance on bioequivalence
• FDA guidance on statistical approaches to bioequivalence (December 2022)
• FDA statistical approaches to BE (January 2001)
• FDA guidance on BE studies (2022)
• FDA product-specific guidances on BE
• Lecture from Helmut Schütz on finding intra-subject variability (or CV%) for power calculations
• You can connect with me on LinkedIn and send me a message
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In this episode, I discuss some nuances of non-compartmental analysis or NCA. I am going to focus on three separate parameters: Cmax, AUC, and the terminal elimination rate constant. These three are the most commonly used parameters to describe drug exposure. I’ll discuss the basic methodology for each and then discuss challenges that may arise when you are performing an analysis. Links discussed in the show: Images mentioned in the podcast Derivation of logarithmic AUC Calculating the elimination rate constant You can connect with me on LinkedIn and send me a message Send me a message Sign up for my newsletter Copyright Teuscher Solutions LLC All Rights Reserved
In this episode, I discuss the 2022 FDA Guidance on Population PK analysis. This includes a discussion of the applications, methods, and submission requirements for population PK models.
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• You can connect with me on LinkedIn and send me a message
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In this episode, I discuss two data analysis techniques: (1) non-compartmental analysis or NCA, and (2) PK modeling. I discuss the basic premise of each method and similarities and differences between them.
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In this episode, I discuss the 2022 FDA guidance on food effect studies. I discuss the timing of these studies in your development programs, various aspects of study design, and some unique exceptions to the standard food effect study.
Links discussed in the show:
• Here’s the current FDA guidance for food effect studies
• You can connect with me on LinkedIn and send me a message
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In my inaugural episode, I introduce myself, describe the type of clinical pharmacology and pharmacometrics content I will share, let you know how to get in touch with me, and give the reason why I started this podcast.
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En liten tjänst av I'm With Friends. Finns även på engelska.