Summary

We’ll introduce the concept of a single platform for modeling and simulation using R. After a brief refresher on basic mrgsolve use (an R package for simulation from ODE-based PKPD and QSP models), we will lead you through case studies reflecting real-world modeling and simulation workflows. The hands-on examples will focus on planning, executing, and presenting simulation-based answers to questions in drug development.
Workshop examples will include:

exploring study designs by interfacing mrgsolve with the optimal design package PopED
generating MAP Bayes estimates from established population PK models
demonstrating several optimizers available in R for parameter estimation in non-hierarchical (deterministic) models
In addition, we’ll review model PLUGINs to extend the mrgsolve model format for more advanced simulation models. This will all lead into an annotated model format and a process for creating modeling simulation documentation in .pdf, .html , or .md format – all on a single-source, open-source, easily-traceable and reproducible compute platform.
Materials Provided to Participants

Course slides, data and code for all examples, online access for 5-days following the workshop to a cloud-based compute server on which the software used in the course is installed.

Requirements
Knowledge and experience in population PKPD modeling and the use of R (or S-PLUS). Previous exposure to the mrgsolve simulation package is encouraged

Course will be led by Metrum’s Kyle Baron, Pharm. D. Ph.D.

Workshop Outline

A brief mrgsolve refresher
Where to find mrgsolve
Where to find help with mrgsolve
Review basic model specification elements
Review basic simulation workflow with mrgsolve
Model parameter estimation in R
Introduction to optimization workflow in R
Using stats::optim
Objective functions for optimization
The optimhelp package
Parameter estimation for PK/PD and QSP models
Organizing inputs and outputs
Other optimizers: minqa, RcppDE, MCMCpack
Generating MAP Bayes estimates in R with mrgsolve
Investigate the influence of residual error variance on MAP Bayes estimates
Interfacing mrgsolve with PopED to explore optimal designs
Planning & organizing simulations to answer questions in development
Multiple strategies for setting up simulations depending on the question and the desired output
Three case studies using population PK/PD and PBPK/QSP models. For each case study we will:
Ask questions that commonly arise in drug development
Develop a technical plan for simulation to address those questions
Simulate and present results
Annotated model specification and rendering model documents
Closing comments, questions, and answers