Pharmacometrics

Objectives

Identify the appropriate class of PK / PD models to describe the different PK / PD relationships.
Describe nonlinear mixed-effects regression models.
Explain the principles of different inference methods for non-linear mixed-effects models.
Propose a model for the temporal evolution of drug concentration and response in various situations.
Use software such as Monolix and R in pharmacometrics.
Interpret the results of pharmacometric analyses.
Explain and communicate the results of a pharmacometric analysis.

Course outline

Introduction to the drug development process
Principles of PK/PD relationships, pharmacokinetic/pharmacodynamic (PK/PD) models
Non-linear regression models (written as differential equations)
Non-linear mixed-effects regression models (maximum likelihood inference, EM algorithm, SAEM)
Model building and evaluation / Monolix
Protocol optimization in simple or mixed nonlinear models, theory and applications, clinical trial simulatio

Prerequisites

Probability, inferential statistics, optimization, R (1A)
Linear regression, GLM, Bayesian computation (2A)
Mixed models (3A)