Search

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00216-020-02407-z) contains supplementary material, which is available to authorized users.

Biofilms are the natural form of life of the majority of microorganisms. These multispecies consortia are intensively studied not only for their effects on health and environment but also because they have an enormous potential as tools for biotechnological processes. Further exploration and exploitation of these complex systems will benefit from technical solutions that enable integrated, machine-assisted cultivation and analysis. We here introduce a microfluidic platform, where readily available microfluidic chips are connected by automated liquid handling with analysis instrumentation, such as fluorescence detection, microscopy, chromatography and optical coherence tomography. The system is operable under oxic and anoxic conditions, allowing for different gases and nutrients as feeding sources and it offers high spatiotemporal resolution in the analysis of metabolites and biofilm composition. We demonstrate the platform’s performance by monitoring the productivity of biofilms as well as the spatial organization of two bacterial species in a co-culture, which is driven by chemical gradients along the microfluidic channel. Helmholtz program BioInterfaces in Technology and Medicine

The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.

Recent studies highlighted the potential of PEGylated proteins to improve stabilities and pharmacokinetics of protein drugs. Ion-exchange chromatography (IEX) is among the most frequently used purification methods for PEGylated proteins. However, the underlying physical mechanisms allowing for a separation of different PEGamers (proteins with a varying number of attached PEG molecules) are not yet fully understood. In this work, mechanistic chromatography modeling is applied to gain a deeper understanding of the mass transfer and adsorption/desorption mechanisms of mono-PEGylated proteins in IEX. Using a combination of the general rate model (GRM) and the steric mass action (SMA) isotherm, simulation results in good agreement with the experimental data are achieved. During linear gradient elution of proteins attached with PEG of different molecular weight, similar peak heights, and peak shapes at constant gradient length are observed. A superimposed effect of increased desorption rate and reduced diffusion rate as a function of the hydrodynamic radius of PEGylated proteins is identified to be the reason of this anomaly. That is why the concept of the diffusion-desorption-compensation effect is proposed. In addition to the altered elution orders, PEGylation results in a considerable decrease of maximum binding capacity. By using the SMA model in a kinetic formulation, the adsorption behavior of PEGylated proteins in the highly concentrated state is described mechanistically. An exponential increase in the steric hindrance effect with increasing PEG molecular weight is observed. This suggests the formation of multiple PEG layers in the interstitial space between bound proteins and an associated shielding of ligands on the adsorber surface to be the cause of the reduced maximum binding capacity. The presented in silico approach thus complements the hitherto proposed theories on the binding mechanisms of PEGylated proteins in IEX.