BackgroundProtein aggregation during monoclonal antibody (mAb) production can occur in upstream and downstream processing (DSP). Current methods to determine aggregate formation during cell culture include size exclusion chromatography (SEC) with a previous affinity chromatography step in order to remove disturbing cell culture components. The pre-purification step itself can already influence protein aggregation and therefore does not necessarily reflect the real aggregate content present in cell culture. To analyze mAb aggregate formation directly in the supernatant of Chinese hamster ovary (CHO) cell culture, we established a protocol, which allows aggregate quantification using SEC, without a falsifying pre-purification step.ResultsThe use of a 3 μm silica SEC column or a SEC column tailored for mAb aggregate analysis allows the separation of mAb monomer and aggregates from disturbing cell culture components, which enables aggregate determination directly in the supernatant. Antibody aggregate analysis of a mAb-producing CHO DG44 cell line demonstrated the feasibility of the method. Astonishingly, the supernatant of the CHO cells consisted of over 75% mAb dimer and larger oligomers, representing a substantially higher aggregate content than reported in literature so far.ConclusionThis study highlights that aggregate quantification directly in the cell culture supernatant using appropriate SEC columns with suitable mAb aggregate standards is feasible without falsification by previous affinity chromatography. Moreover, our results indicate that aggregate formation should be addressed directly in the cell culture and is not only a problem in DSP.Electronic supplementary materialThe online version of this article (doi:10.1186/s12896-014-0099-3) contains supplementary material, which is available to authorized users.

Product yields, efficacy, and safety of monoclonal antibodies (mAbs) are reduced by the formation of higher molecular weight aggregates during upstream processing. In-process characterization of mAb aggregate formation is a challenge since there is a lack of a fast detection method to identify mAb aggregates in cell culture. In this work, we present a rapid method to characterize mAb aggregate-containing Chinese hamster ovary (CHO) cell culture supernatants. The fluorescence dyes thioflavin T (ThT) and 4-4-bis-1-phenylamino-8-naphthalene sulfonate (Bis-ANS) enabled the detection of soluble as well as large mAb aggregates. Partial least square (PLS) regression models were used to evaluate the linearity of the dye-based mAb aggregate detection in buffer down to a mAb aggregate concentration of 2.4 μg mL(-1). Furthermore, mAb aggregates were detected in bioprocess medium using Bis-ANS and ThT. Dye binding to aggregates was stable for 60 min, making the method robust and reliable. Finally, the developed method using 10 μmol L(-1) Bis-ANS enabled discrimination between CHO cell culture supernatants containing different levels of mAb aggregates. The method can be adapted for high-throughput screening, e.g., to screen for cell culture conditions influencing mAb product quality, and hence can contribute to the improvement of production processes of biopharmaceuticals in mammalian cell culture.

Online monitoring of Chinese hamster ovary fed-batch cell cultures via two-dimensional fluorescence spectroscopy (2DFS) was evaluated in this work. Particular attention was directed toward different process strategies regarding the use of nutrient-rich feed media and temperature shifts. These intentionally performed process manipulations broadened the variances in the obtained fluorescence spectra and this was suspected to hamper the generation of reliable soft sensors. Principal component analysis of the obtained fluorescence data showed that temperature shift and feeding strategy had a considerable impact on the fluorescence signals. Partial least square regression models were calculated for the prediction of glucose, lactate, monoclonal antibody (mAb), and viable cell concentrations (VCC). It was aimed to integrate all 2DFS datasets in the respective calibration models regardless of the process-strategy-dependent diversity. Contrary to the expectations, it was feasible to calibrate soft sensors for the online prediction of glucose (7 latent variables (LVs), Rcal2 = 0.97, rout mean squared error of prediction (RMSEP) = 1.1 g L ), lactate (5 LV; Rcal2 = 0.96; RMSEP = 0.5 g L ) and mAb concentrations (4 LV; Rcal2 = 0.99; RMSEP = 11.4 mg L ). Feeding and temperature shifts had the highest impact on the VCC model (3 LV; Rcal2 = 0.94; RMSEP 3.8 × 10 mL ), nevertheless the prediction of VCC from the fed-batch 2DFS data was feasible. The results strongly indicate that variances in the datasets due to the process strategy can be tolerated to some extent by the respective soft sensors. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1592-1600, 2016.

In this work, the viability of using aqueous-phase sugars derived from the pyrolysis of lignocellulosic biomass was analyzed using high-throughput screening in microtiter plates. To standardize results, a synthetic aqueous phase of pyrolytic biooil was constructed based on typical constituents and composition ranges and then used to determine the fermentation viability of Saccharomyces cerevisiae. The effects of inhibitory compounds in pyrolytic bio-oil were assessed by fitting measured growth kinetics to the model of Baranyi and Roberts (Int. J. Food Microbiol. 1994, 23, 277), specifically on the fitted growth rates, initial microorganism adaptation, and maximum biomass densities. It was found that even a dilution to approximately 10% of the hypothetical inhibitor concentration in aqueous bio-oil was significantly inhibitory to growth, although the presence of additional sugars was able to moderate this impact slightly. The high-throughput screening used in this work allowed for the rapid measurement of a variety of inhibitors at different concentrations, as well as inhibitory mixtures.

The glycosyltransferase HisDapGalNAcT2 is the key protein of the Escherichia coli (E. coli) SHuffle® T7 cell factory which was genetically engineered to allow glycosylation of a protein substrate in vivo. The specific activity of the glycosyltransferase requires time-intensive analytics, but is a critical process parameter. Therefore, it has to be monitored closely. This study evaluates fluorometric in situ monitoring as option to access this critical process parameter during complex E. coli fermentations. Partial least square regression (PLS) models were built based on the fluorometric data recorded during the EnPresso® B fermentations. Capable models for the prediction of glucose and acetate concentrations were built for these fermentations with rout mean squared errors for prediction (RMSEP) of 0.19 g·L−1 and 0.08 g·L−1, as well as for the prediction of the optical density (RMSEP 0.24). In situ monitoring of soluble enzyme to cell dry weight ratios (RMSEP 5.5 × 10−4 µg w/w) and specific activity of the glycosyltransferase (RMSEP 33.5 pmol·min−1·µg−1) proved to be challenging, since HisDapGalNAcT2 had to be extracted from the cells and purified. However, fluorescence spectroscopy, in combination with PLS modeling, proved to be feasible for in situ monitoring of complex expression systems.