Pharmacometrics is new discipline that originated from the need to integrate model-based techniques applied mainly in clinical drug development. In this 3-days hands-on workshop, basic elements of pharmacometrics are introduced and explored: Drug development phases, pharmacokinetics and pharmacodynamics, nonlinear mixed-effects modeling, and clinical trial simulation.
To alleviate or cure human diseases, drug treatment plays a major role. However, the development of innovative drugs is becoming more and more challenging. Traditional experimental methods alone cannot cope with the increasing factual knowledge about the functioning of a diseased human being. Particularly, how drugs exert their action and how they are optimally administered is a problem too big for pure experimentation. Actually, drug development suffers from many costly failures.
A natural means to overcome such shortcomings is to combine experimentation with mathematical modeling. Over the last 30 years, different groups within and outside the pharmaceutical industry suggested the application of “Model-based drug development”, “Systems Biology”, or “Quantitative Systems Pharmacology”. All these terms address the importance of mathematical modeling in the development of drugs.
Progress has been made in that nowadays the outcome of a clinical trial is simulated beforehand leading to better trial designs. Simulating clinical trials outcome comprises the quantitative consideration of how the drug reaches the site of action, how it acts on the body, and how disease processes are affected. Importantly, between and within variability in all measured responses needs to be taken into account.
At the end of the course, you will

• have an overview of Pharmacometrics and its promise for the pharmaceutical industry,
• have a better understanding on Pharmacokinetics (i.e., how to quantitatively model the uptake, level at the target organ, and elimination of drugs across time) and Pharmacodynamics (i.e., how to quantitatively model the effects of drugs on the diseased body)
• know how to combine exact mathematical modeling with insights from life science
• appreciate Mathematica as a tool to easily solve and explore systems of differential equations