(742d) Raman Spectroscopy- Towards the Prediction of Quality Attributes and Application in Cell Culture Process Development

Feidl, F., ETH Zurich
Sokolov, M., ETH Zurich
Broly, H., Merck Serono S.A.
Morbidelli, M., ETH Zürich
Butté, A., ETH Zurich
In recent years, the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have stressed the need of improved process understanding and control in the area of pharmaceutical development, manufacturing and quality assurance, summarized in the Process Analytical Technology (PAT) initiative. Lately, Raman spectroscopy has emerged as a promising online monitoring and control tool in bioprocesses. The advantages of this technology are the invasive and nondestructive operations, the simultaneously detection of multiple process variables, as well as the online operation mode and real-time information release.

Raman spectroscopy is mainly used for monitoring purposes at production stage, where variations are small and the process is already well defined. However, the exploitation of Raman technology for process development, where different process conditions, media, products or cell lines are tested resulting in a large variability and few experiments at similar conditions, could bring very important benefits, in terms of process understanding, speed and cost reduction. This work presents several ideas, experimental results and recommendations to overcome those challenges in process development.

Different fed-batch cultures, varying in cell lines, products and platform media, were monitored online. In addition, diverse spiking strategies were performed to artificially increase the calibration range as well as the quality of the prediction models. The evaluation of advanced modeling techniques to derive optimal results from the obtained spectral data plays a central role in this work. Those techniques included an in-depth analysis of the pretreatment procedure, wavelength selection and outlier elimination with regards to robust calibration and prediction of the components of interest.

Besides classical parameters such as glucose, lactate, titer and viable cell density, all 20 amino acids could be very well predicted. More than that, even the prediction of quality attributes like aggregates and glycans showed promising results. In addition, the potential of generic models, which can be applied independently of the cell line, process platform and medium, could be proven. This enables the Raman spectroscopy as an online monitoring tool of multiple process variables and its implementation into a supervisory control of a continuous integrated bioprocess to ensure consistent process efficiency and shaping the product qualities.