No abstract

Aggregation of therapeutic proteins is a major concern as aggregates lower the yield and can impact the efficacy of the drug as well as the patient's safety. It can occur in all production stages; thus, it is essential to perform a detailed analysis for protein aggregates. Several methods such as size exclusion high-performance liquid chromatography (SE-HPLC), light scattering, turbidity, light obscuration, and microscopy-based approaches are used to analyze aggregates. None of these methods allows determination of all types of higher molecular weight (HMW) species due to a limited size range. Furthermore, quantification and specification of different HMW species are often not possible. Moreover, automation is a perspective challenge coming up with automated robotic laboratory systems. Hence, there is a need for a fast, high-throughput-compatible method, which can detect a broad size range and enable quantification and classification. We describe a novel approach for the detection of aggregates in the size range 1 to 1000 μm combining fluorescent dyes for protein aggregate labelling and automated fluorescence microscope imaging (aFMI). After appropriate selection of the dye and method optimization, our method enabled us to detect various types of HMW species of monoclonal antibodies (mAbs). Using 10 μmol L 4,4'-dianilino-1,1'-binaphthyl-5,5'-disulfonate (Bis-ANS) in combination with aFMI allowed the analysis of mAb aggregates induced by different stresses occurring during downstream processing, storage, and administration. Validation of our results was performed by SE-HPLC, UV-Vis spectroscopy, and dynamic light scattering. With this new approach, we could not only reliably detect different HMW species but also quantify and classify them in an automated approach. Our method achieves high-throughput requirements and the selection of various fluorescent dyes enables a broad range of applications.

Cell‐free protein expression is a promising tool for improving protein‐specific expression techniques. Despite their advantages, insect cell‐free expression systems are not as well established as Escherichia coli cell‐free systems. In most studies, characterization and optimization strategies are based on manual “one‐factor‐at‐a‐time” investigations that are expensive and time consuming. In this paper, two insect cell‐free expression systems (Sf9 and High Five™) were reproducibly (CV = 2.9%) implemented on a robotic platform with integrated analytics. All experiments were planned by statistical design of experiments using central composite designs and analyzed by multivariate data analysis. Quadratic response surface models were fitted to the experimental data and model predictivity was validated successfully for both insect cell types. The characterization of the complete in vitro translation process included quantification and visualization of the parameter influences on the expression yield and the robustness of the systems. The results were compared to previous studies, which confirmed the applicability of the new method. In the future, yields from insect cell‐free expression can be enhanced using a comprehensive system characterization based on optimally designed high‐throughput screenings on robotic systems.