A Gentle Introduction to OptimalDesign for Pharmacometric Models

PK/PD studies should be designed in such a way that the model parameters will be estimated with adequate precision and bias. This can be assessed by simulation, but depending on the study and model(s) involved, it can be impractical to evaluate many combinations of design variables. Optimal design tools allow us to quickly evaluate designs and even search over a design space for the best possible design.

In this webinar (recorded on June 8, 2020), we will introduce basic concepts of optimal design, and then present examples of how to inform PK sampling time selection using the R packages PopED and mrgsolve.

This webinar is part of Metrum Research Group’s Cellar Office OPen-EDUcation in Pharmacometrics (“COOPED UP”) events, which feature a range of topics, discussed in vignette formats, to share with our community the same training and development in which our staff routinely engages.

R code from this course can be found at https://github.com/metrumresearchgroup/optimal-design

 

Introduction to mrgsolve (PAGE workshop, 2018)

This course was delivered at the PAGE 2018 meeting in Montreux, Switzerland on June 2, 2018.

This workshop provides a guided hands-on experience in the use of the R package mrgsolve. mrgsolve is a free, open source, validated R package to facilitate simulation from hierarchical, ODE-based PK/PD and systems pharmacology models frequently employed in pharmaceutical research and development programs. You will code, execute, and summarize PK, PK/PD, and systems pharmacology model simulations using mrgsolve and R.  Through many examples, you will learn to implement model-based simulations to help address questions at a variety of stages of a development program.

Advanced Use of Stan, RStan and Torsten for Pharmacometric Applications

These videos capture most of a one day workshop presented at the PAGE 2018 meeting in Montreux, Switzerland on 29 May 2018.

The workshop provides a guided hands-on experience in the advanced use of Stan, rstan and Torsten for Bayesian PKPD modeling. Stan is a flexible open-source software tool for Bayesian data analysis using Hamiltonian Monte Carlo (HMC) simulation—a type of MCMC simulation. Torsten is a Stan extension containing a library of functions to simplify implementation of PKPD models. This workshop builds on the foundations presented in our previous introductory Stan workshops. Topics include model evaluation and comparison, models with systems of ODEs, optimizing Stan code, using MCMC results for population and trial simulations, and more. You will execute Bayesian data analysis examples using Stan. Via the examples, you will learn to implement population PKPD models including those involving censoring, numerical solution of ODEs, and user-defined probability distributions and likelihoods.

Download the zip file containing the course materials. Unzip the file. Navigate to the “script” directory. Open the R script file “pkgSetup.R” using your favorite R environment and run the script to install the R packages used in the course.

MI212: Advanced Topics in Population PK-PD Modeling & Simulation

MI212 covers intermediate through advanced-level population PK-PD modeling and simulation through lecture and hands-on lab sessions. Topics covered include for nonlinear PK models, modeling PK data with BQL records, models for parent-metabolite data, models for plasma and urine PK data, indirect PK-PD models, disease progression models and clinical trial simulations. This course makes extensive use of NONMEM® 7 and R, as well as the MIfuns package.

MI210: Essentials of Population PK-PD Modeling and Simulation

MI210 provides an extensive overview of topics in population pharmacokinetics and pharmacodynamics, including nonlinear mixed-effects modeling theory and implementation, data formatting requirements, population model development, model evaluation techniques, continuous PK-PD models, Monte Carlo simulation, and best practices. Instruction combines didactic lectures with hands-on exercises using R, MIfuns, and the NONMEM® 7 software.