Biopharmaceuticals produced with mammalian cells are the key drivers for the medication of former untreatable diseases. The development of their production processes is still challenging because of regulatory requirements and complex and sophisticated process strategies. Development and scale-up of cell culture processes are still mainly based on expert knowledge, one-factor-at-a-time or experimantal Design of Experiments approaches. This significantly increases the time and costs and simultaneously limits the obtained knowledge.

New design and process control strategies mainly based on mathematical process models are en route from academia to industry. The key component is a model, which aims to describe the real phenomena as simple as possible and as accurate as necessary. Model-assisted methods can therefore increase the process understanding, process control and monitoring and shorten the development times for cell culture processes. 

Learning Objectives:

1. Discuss the potential of a model-assisted process design for a pharmaceutical production processes.

2. Explain coupling of statistical design of experiment (DoE) methods and mathematical models.

3. List examples for model-assisted DoE for the reduction of experimental effort as well as the model-assisted interpretation and evaluation of process management strategies.