Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real‐time during chromatographic separation

Technological developments require the transfer to their location of application to make use of them. We describe the transfer of a real‐time monitoring system for lab‐scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF‐2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, and MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real‐time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test run at the new sites were on average twice as high as at the training site (model‐wise 0.9–5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1% and 33%. The authors show the challenges to transfer real time monitoring systems based on highly sensitive sensors and predictive models from the process science lab to two sites. Even though the exact same sensor set‐up was used at all three sites, the main challenge was the different sensitivity of sensors and data deviations due to non‐standardized process protocols. Partially, such deviations were compensated by data pre‐processing. Adaptions of the models considering those challenges enabled monitoring of in‐house processes with good precision.