Latest posts

Inter-individual and/or inter-occasion variability: what can we quantify in our models and what is the impact on simulations

Introduction We are not all the same. We know that there is variability originating from physiological differences in the pharmacokinetic and pharmacodynamic (PK/PD) processes between individuals in a population, also called the inter-individual variability or IIV. We also know that the there can be changes in these processes from one day to another, or between two dose administrations, which is …

Calculating the power of covariates in population non-linear mixed effects models: the Monte Carlo Mapped Power approach

This post is based on the work of, among others, Camille Vong and the hands-on course about the MCMP given by Rob ter Heine and Elin Svensson. Read and cite the following publications when using an MCMP analysis in your next project: https://link.springer.com/article/10.1208%2Fs12248-012-9327-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4791488/ Power You have probably heard people talk about the power of a study. In short, what …

Flawed study design of parent-metabolite pharmacokinetic studies – the prove is in the pee

How do we develop a ‘standard’ population PK model? We obtain blood/plasma concentrations over time of the drug of interest, in multiple individuals, and we apply our population NLME modelling techniques to quantify the parameters. This data is sufficient to provide us with information on the absorption rate constant, the volume of distribution, the clearance, and the inter-individual variability in …

Celebrating 15.000 visitors by going open source!

In 2019 alone, PMXSolutions.com has been visited by over 15.000 visitors! In order to celebrate this overwhelming interest in the website, I have made the Shiny application for pharmacokinetic simulations open source on GitHub! Visit the GitHub repository https://github.com/michielve/PMX_Simulations Collaborate on code GitHub is a great platform to share and collaborate on coding projects. Feel free to use and improve …

Modelling pulsatile profiles in NONMEM

A modellers challenge: getting data in front of you that don’t seem to fit any of the standard effect models and PKPD relationships. Especially in endocrinology, hormonal profiles are rarely in steady-state and vary constantly over time, complicating the application of NLME models. In this post I will discuss 3 degrees of pulsatile profiles on 3 model hormones: Circadian rhythm …

Building your first PBPK model:the basics

In previous posts we referred exclusively to modelling using the top-down, population approach. However, in recent years, physiology-based, bottom-up approaches are getting more attention from both industry and regulators. Population and physiology-based approaches share some common ground: both approaches describe the body as a system of compartments connected by rates to describe drug disposition. If, population approaches use 1, 2 …

Plotting PK/PD hysteresis with variability in R using ggplot

The identification of hysteresis in a PK/PD relationship provides information on a possible delay between the plasma concentration and the effect. The identification of hysteresis can further assist us in structural PK/PD model development by the inclusion of an effect compartment. More information on hysteresis can be found on the following pages: https://www.certara.com/2011/10/26/what-is-hysteresis-in-pkpd-analysis/? https://www.ncbi.nlm.nih.gov/pubmed/24735761 In this post, I will show …

Modelling asymmetry in concentration-effect relationships

Introduction Why do we assume symmetry in our concentration-effect relationship? I recently came across an article published by Piet Hein van der Graaf and Rik Schoemaker from 1999 on the use of asymmetry in concentration-effect curves. “Analysis of asymmetry of agonist concentration–effect curves”: https://www.sciencedirect.com/science/article/pii/S105687199900026X They make a valid point on an assumption we commonly do not investigate: a logistic or …

Validate your model with NPDE analysis

As part of the series of tutorials on model validation, I will get you started on doing your own Normalized Prediction Distribution Errors (NPDE) analysis.  As the VPC and bootstrap, the NPDE is also a simulation-based evaluation tool. NPDEs are useful to investigate the accuracy of the model predictions, and I find it particularly useful when working with models based …

Modelling & Simulation at ASCPT2019 – a short impression

The annual meetings of the American Society for Clinical Pharmacology & Therapeutics have been for me the most inspiring conferences that I visited. This year’s meeting did not disappoint either, here is why. I feel that the PAGE meeting in Europe discusses modelling and simulation from a more technical perspective, whereas the perspective of the ASCPT annual meeting is more …

Get started to non-parametric bootstraps: execution and interpretation

Once you completed your model development, you now have a final model that fits your data the best and you’re now ready to validate your results. One way to internally validate your results is by looking at the precision of your parameter estimates by performing a non-parametric bootstrap. What is a bootstrap? In the figure below I tried to illustrate …

Simulating your pharmacometric model with parameter uncertainty in R

How certain are we about the parameters that we estimate in our population model? Is that volume of distribution that NONMEM gave us really 10 liters with a clearance of 5 L/h? Or can it also be 9L and 6L/h respectively? And what will the impact of this uncertainty be on your simulations? This is the issue that we call …