Dr. Christoph Herwig, Professor of Bioprocessing Engineering at Vienna University of Technology, and Founder and Scientific Advisor of  Exputec GmbH recently shared insights on his upcoming presentation "Quality by Design-Driven Approaches for Reaching Process Robustness", to be delivered at the 14th annual Optimizing Cell Culture Technology conference at the Bioprocessing Summit in Boston.

Quality by Design-Driven Approaches for Reaching Process Robustness

Christoph Herwig, PhD, Professor, Bioprocessing Engineering, Vienna University of Technology (TU Wien), and Founder and Scientific Advisor, Exputec GmbH

Cell culture processes still lack thorough understanding in reaching higher titers and process robustness using process technological means. This contribution shows different aspects of Quality by Design-driven approaches for limiting amino acids using PAT and automated modelling approaches; model-based experimental designs for targeting maximum information content of the experiment for a given objective, such as media titer optimization or lactate uptake, and scale-down model establishment to analyze the effect of large-scale inhomogeneities.

Q. How can process technological techniques help culture cells and lead to greater productivity?

Process technological techniques are elements, such as physiological feeding and model-based controls, based on sound science. The main output is process understanding. However, process understanding is a flurry buzzword. Essentially what we need is a clear relationship between inputs and outputs of each unit operation, but also along the entire process chain. By projecting process understanding in model-based controls, we can reduce process variability and optimize to objective function, such as higher titer, higher productivity; but also target product quality using model predictive control techniques.

Q. How do you use QbD and modeling to help cultivate cells?

QbD methodology is a set of tools, which is around since many years. Process Analytical Technology (PAT) and Design of Experiments (DoE) are some examples, however QbD is much more than measuring in real time and making multivariate experiments. This is the reason why the potential of QbD is still underestimated. We need workflows combining the tools in business processes along pharmaceutical quality systems (ICHQ10) but also more intelligent experimental designs such as optimum model-based designs; we have to use our brain. This includes the use of a large portion of statistics but also mechanistic understanding, which we unravel using data science techniques. Through this technology, we understand, how cells are happy and can be cultivated also for prolonged periods enabling continuous biomanufacturing.

Q. Can you describe how you incorporate scale-down models as part of the cell culture process?

The development and use of scale-down models are regulatory imperatives of process characterization studies in Stage 1 Validation. They enable the efficient justification of normal operating ranges (NOR) and of course the root cause analysis of process variability in manufacturing. For that, the scale-down model needs to be qualified. We do two steps for this: a) Analyze main scale-up effects of process parameters, such as analyzing the decoupled effect of CO2 and pH, as well as analyzing the effect of zones of inhomogeneity on process performance. b) Apply proper data science algorithms to analyze comparability between scales to statistically state that scale as significantly equivalent.

Q. Do you encounter mis-conceptions about implementing QbD and process technologies?

As mentioned above, we cannot take the tools of QbD without the clear willingness to understand. We have to do more than just run DoEs in non-justified ranges with colorful pictures and doubtful experimental quality displayed in any R2 and Q2 values. We, for example, also need to analyze non-significant but critical effects of process parameters on quality attributes, we need to know the data quality from our (PAT) sensors and what to expect from the sampling plan. For understanding, we need to know what the cell experiences along its life cycle: Just the measurement of viable cell count and viability may not be sufficient to state a stable perfusion process. This may result in high variability in downstream processes and the path to continuous biomanufacturing will not be successful.

Q. What are you most looking forward to at The Bioprocessing Summit?

We need to cross fertilize from upstream to downstream, between new modalities and established (commodity) cell culture processes and between various scientific methodologies in order to achieve affordable drugs, economic benefits, patent safety and novel products. The Bioprocessing Summit provides a great opportunity to do so, as including a substantial part of science, such as process and data science as well as by nicely designed tracks and excellent networking opportunities.

Christoph Herwig, PhD, Professor, Bioprocessing Engineering, Vienna University of Technology (TU Wien), and Founder and Scientific Advisor, Exputec GmbH

Dr. Herwig possesses extensive research experience in quantification, error detection, and identification of bio-process performance, including application of novel techniques for on-line monitoring & control. In addition, he has comprehensive research understanding in microbial physiology and strain characterization tasks using dynamic process conditions. His background includes thorough experience in managing complex engineering projects with up to 40 team members inside functionality, time and cost in biopharmaceutical industry. Dr. Herwig’s PhD comes from École polytechnique fédérale de Lausanne (EPFL) in Bioprocess Engineering.