Noninvasive online biomass detector system for cultivation in shake flasks

A novel online sensor system for noninvasive and continuous monitoring of cell growth in shake flasks is described. The measurement principle is based on turbidity measurement by detecting 180°‐scattered light and correlation to OD by nonlinear calibration models. The sensor system was integrated into a commercial shaking tablar to read out turbidity from below the shake flasks bottom. The system was evaluated with two model microorganisms, Escherichia coli K12 as prokaryotic and Saccharomyces cerevisiae as eukaryotic model. The sensor allowed an accurate monitoring of turbidity and correlation with OD₆₀₀ ≤ 30. The determination of online OD showed relative errors of about 7.5% for E. coli K12 and 12% for S. cerevisiae. This matches the errors of the laborious offline OD and thus facilitates to overcome the drawbacks of the classical method as risk of contamination and decreasing volumes through sampling. One major challenge was to ensure a defined, nonvarying measurement zone as the rotating suspension in the shake flask forms a liquid sickle which circulates round the flasks inner bottom wall. The resulting alteration of liquid height above the sensor could be compensated by integration of an acceleration sensor into the tablar to synchronize the sensor triggering.     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.     

Real‐time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures

The glycosylation of therapeutic monoclonal antibodies (mAbs), a known critical quality attribute, is often greatly modified during the production process by animal cells. It is essential for biopharmaceutical industries to monitor and control this glycosylation. However, current glycosylation characterization techniques involve time‐ and labor‐intensive analyses, often carried out at the end of the culture when the product is already synthesized. This study proposes a novel methodology for real‐time monitoring of antibody glycosylation site occupancy using Raman spectroscopy. It was first observed in CHO cell batch culture that when low nutrient concentrations were reached, a decrease in mAb glycosylation was induced, which made it essential to rapidly detect this loss of product quality. By combining in situ Raman spectroscopy with chemometric tools, efficient prediction models were then developed for both glycosylated and nonglycosylated mAbs. By comparing variable importance in projection profiles of the prediction models, it was confirmed that Raman spectroscopy is a powerful method to distinguish extremely similar molecules, despite the high complexity of the culture medium. Finally, the Raman prediction models were used to monitor batch and feed‐harvest cultures in situ. For the first time, it was demonstrated that the concentrations of glycosylated and nonglycosylated mAbs could be successfully and simultaneously estimated in real time with high accuracy, including their sudden variations due to medium exchanges. Raman spectroscopy can thus be considered as a promising PAT tool for feedback process control dedicated to on‐line optimization of mAb quality. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:486–493, 2018     

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.