Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements

Abstract

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control.

Monitoring Ethanol Fermentation in Real Time by a Robust State Observer for Uncertainties

“…The optical densities of the samples were measured spectrophotometrically at 230 nm using a cellfree medium as a control. The dry weight of the cells in free suspension was determined by filtering a fixed volume of cell suspension through previously weighed 0.22 μm filter paper with the help of a vacuum pump, by drying the filter papers in Petri dishes at 60 °C for three days, and then by weighing them again [39][40][41][42][43].…”

“…The incubation temperature was 30 °C, and the agitation was 150 rpm [40]. The fermentation time of 25 h was established by considering the results of kinetics obtained in previous works [39–43].…”

“…In 2023, Rolinger et al used a real-time Kalman filter in a CSTR, assuming that the detained flow is much larger than the permeate flow and that the process volume remains constant. Currently, an application is being developed in MATLAB that communicates and controls all embedded sensors and devices [39]. As shown, there are few works where real-time implementations have been reported because of modeling errors, uncertainties, and the highly nonlinear nature of bioprocesses.…”

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

“…The smoothness constraint of the tted baseline is characterised by the sum of second-order differences (SSD), as in eqn (2).…”

“…Spectral analytical techniques such as infrared spectroscopy, 1 Raman spectroscopy, 2 and laser-induced spectroscopy 3 have been widely used in many elds (e.g., energy, environmental, chemical industry, agriculture, and medicine) because of their fast, convenient, accurate, highly sensitive, and non-destructive characteristics. [4][5][6][7] Spectral data usually contain baseline dri due to changes in the measurement environment, and the inuence of the instrument itself.…”