Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance‐based modeling

Abstract

The production of biopharmaceuticals requires highly sophisticated, complex cell based processes. Once a process has been developed, acceptable ranges for various control parameters are typically defined based on process characterization studies often comprising several dozens of small scale bioreactor cultivations. A lot of data is generated during these studies and usually only the information needed to define acceptable ranges is processed in more detail. Making use of the wealth of information contained in such data sets, we present here a methodology that uses performance data (such as metabolite profiles) to forecast the product quality and quantity of mammalian cell culture processes based on a toolbox of advanced statistical methods. With this performance based modeling (PBM) the final product concentration and 12 quality attributes (QAs) for two different biopharmaceutical products were predicted in daily intervals throughout the main stage process. The best forecast was achieved for product concentration in a very early phase of the process. Furthermore, some glycan isoforms were predicted with good accuracy several days before the bioreactor was harvested. Overall, PBM clearly demonstrated its capability of early process endpoint prediction by only using commonly available data, even though it was not possible to predict all QAs with the desired accuracy. Knowing the product quality prior to the harvest allows the manufacturer to take counter measures in case the forecasted quality or quantity deviates from what is expected. This would be a big step towards real-time release, an important element of the FDA's PAT initiative.

Combining mechanistic and data‐driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed‐batch CHO process

“…Wahrheit et al recently demonstrated how the combination of MFA, linear regression, and heat maps can be used to identify the effect of experimental factors such as glutamine concentration on intracellular fluxes . Although PLS‐R and heat maps have been successfully used for knowledge extraction from large bioprocess development datasets, the combination of multivariate data analysis methods with metabolic flux analysis has not been reported in the literature to our knowledge.…”

Sequential Multivariate Cell Culture Modeling at Multiple Scales Supports Systematic Shaping of a Monoclonal Antibody Toward a Quality Target

“…In the last decade, several trends could be observed in biopharmaceutical process development: a shift from yield maximization to quality optimization, the utilization of miniaturized and parallelized high throughput techniques, single‐use technologies, continuous bioprocessing, and continuous data acquisition as well as the utilization of data‐ and knowledge‐driven tools for process analysis, forecasting, monitoring, and control . Many of these trends are already well‐established in the “older” industries such as automotive.…”

Opportunities and challenges of real‐time release testing in biopharmaceutical manufacturing

“…This approach successfully removed the end‐product release testing of these product qualities and provided an avenue for RTRT using product quality predictive models established based on process understanding. In similar way, Sandoz scientists have demonstrated prediction of multiple PQAs based on commonly available process parameters using modelling termed as performance‐based modeling (Schmidberger, Posch, Sasse, Gulch, & Huber, ).…”