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%.

Scale up of a chromatographic capture step for a clarified bacterial homogenate - Influence of mass transport limitation and competitive adsorption of impurities.

A model-based approach for scaling up chromatographic capture step was developed. The purification of human basic fibroblast growth factor protein 2 (FGF2) from an E. coli homogenate on a cation exchange resin was selected as a case study. Non-ideal effects accompanying the capture operation were examined, including: reduction in the protein diffusivity in the presence of the homogenate, competitive adsorption between FGF2 and undefined impurities, and flow behavior in external column volumes. The viscosity of the homogenate was measured as a function of dilution degree and shear stress, and its contribution to the diffusivity reduction was quantified. A dynamic model was formulated which accounted for underlying kinetic and thermodynamic dependencies. The model parameters were determined for a lab scale system using a small 2-mL column. The model was successfully used to scale up the capture operation from the lab scale column to a preparative bench scale column of about 1 L volume.

Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2.

Process analytical technology combines understanding and control of the process with real-time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi-angle light scattering, refractive index, attenuated total reflection Fourier-transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double-stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co-eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1-28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier-transform infrared spectral information was important to achieve prediction errors of 200 (2-6579 ppm) and 340 ppm (8-3773 ppm), respectively.

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time.

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid-infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real-time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time- and labor-intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

A two-step process for capture and purification of human basic fibroblast growth factor from E. coli homogenate: Yield versus endotoxin clearance.

A two-step purification process for human basic fibroblast growth factor (FGF-2) from clarified E. coli homogenate has been developed in which the impurity level after the second step is below the limit of quantification. Endotoxin content is cleared to 0.02 EU/µg FGF-2 and the overall yield is 67%. The performance of the cation exchanger Carboxymethyl-Sepharose Fast Flow (CM-SFF) was compared to the affinity resin Heparin-SFF regarding the impurity profile and product quality in the elution peak. The CM-SFF eluate was further purified using hydrophobic interaction resin Toyopearl-Hexyl-650C. The relative amounts of target product, host cell proteins (HCPs), dsDNA, endotoxin, monomer content, and high molecular weight impurities differed along the elution peak depending on the applied method. The bioactive monomer (>99%) was obtained with a yield of 48% for CM-SFF and 68% for Heparin-SFF. A half-load reduction in CM-SFF increased the yield up to 67% without deterioration of the impurity content. Assuming a dose of 400 µg FGF-2, endotoxin was reduced to 188 EU/dose, dsDNA <10 ng/dose, and HCP <2 ppm/dose using the cation exchanger. In the pooled eluate fractions, dsDNA was removed 4-fold (291 ng/mL) and endotoxin 14-fold (0.47 EU/µg FGF-2) more efficiently by CM-SFF than by affinity chromatography. In contrast, HCP clearance was 3-fold (13 ppm) more efficient with Heparin-SFF than CM-SFF. In contrast to process monitoring by UV280nm or SDS-PAGE, this characterization is the basis for a Process Analytical Technology attempt when correlated with online monitored signals, as it enables knowledge-based pooling according to defined quality criteria.

Prediction tool for loading, isocratic elution, gradient elution and scaling up of ion exchange chromatography of proteins.

An efficient mathematical tool for the design and scaling up of protein chromatography is suggested, in which the model parameters can be determined quickly over a wide operating space without large material investments. The design method is based on mathematical modelling of column dynamics and moment analysis. The accuracy of the dynamic models that are most frequently used for simulations of chromatographic processes is analyzed, and possible errors that can be generated using the moment analysis are indicated. The so-called transport dispersive model was eventually employed for the process simulations. The model was modified to account for the protein dispersion in void volumes of chromatographic systems. The manner of the model calibration was suggested, which was based on a few chromatographic runs and verified over a wide space of the operating parameters, including composition and flow rate of the mobile phase, column dimensions, residence time, and mass loading. The model system for the study was ion-exchange chromatography. The analysis was performed based on the elution profiles of basic fibroblast growth factor 2 and lysozyme, on two different IEX media.

The putative protein methyltransferase LAE1 controls cellulase gene expression in Trichoderma reesei.

Trichoderma reesei is an industrial producer of enzymes that degrade lignocellulosic polysaccharides to soluble monomers, which can be fermented to biofuels. Here we show that the expression of genes for lignocellulose degradation are controlled by the orthologous T. reesei protein methyltransferase LAE1. In a lae1 deletion mutant we observed a complete loss of expression of all seven cellulases, auxiliary factors for cellulose degradation, ß-glucosidases and xylanases were no longer expressed. Conversely, enhanced expression of lae1 resulted in significantly increased cellulase gene transcription. Lae1-modulated cellulase gene expression was dependent on the function of the general cellulase regulator XYR1, but also xyr1 expression was LAE1-dependent. LAE1 was also essential for conidiation of T. reesei. Chromatin immunoprecipitation followed by high-throughput sequencing ('ChIP-seq') showed that lae1 expression was not obviously correlated with H3K4 di- or trimethylation (indicative of active transcription) or H3K9 trimethylation (typical for heterochromatin regions) in CAZyme coding regions, suggesting that LAE1 does not affect CAZyme gene expression by directly modulating H3K4 or H3K9 methylation. Our data demonstrate that the putative protein methyltransferase LAE1 is essential for cellulase gene expression in T. reesei through mechanisms that remain to be identified.