Introduction: Pharmacometrics, a vital part of drug development and clinical pharmacology, is themathematical study of the relationship between a treatment’s administered dose, its concentration in the body,and its measured effect, and how these evolve with time. This module will cover the theoretical underpinningsof pharmacometrics and provide computer laboratory hands-on data analysis experience.Aims of the course: This course aims to introduce the theory and application of nonlinear mixed effectsmodelling in clinical pharmacology and pharmacometrics.Objectives of the course:1. Review the basic concepts of pharmacokinetics and pharmacodynamics2. Introduce mathematical modelling and parameter estimation for linear and nonlinear models3. Introduce mixed effects (or multi-level) modelling4. Introduce model evaluation using diagnostics5. Introduce covariate analysis6. Introduce the Bayesian method for nonlinear mixed effects model parameter estimationApplications: Applications discussed will focus on pharmacokinetic (PK) and pharmacodynamic (PD) orpharmacometric modelsCourse content: The course will consist of 10 lectures and 10 practical hands-on computer-based labs usingR and NONMEM. The course notes also provide some further questions and optional exercises that will not becovered in the hands-on, but should prove useful for self-study.Pre-requisites:1. Basic calculus and knowledge of statistical distributions2. Basic R programming skillsAssessment: In course assessment (worth 20%) 1000 word report of pharmacometric modelling assignmentwritten in R-markdown assigned Week 6, deadline Week 8. Written exam 2 hours (worth 80%).Module Leader:Prof Joe Standing: j.standing@ucl.ac.ukTexts:Bonate PL. 2011. Pharmacokinetic-Pharmacodynamic Modeling and Simulation. Springer.