Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its ca- pacity to selectively capture and separate target molecules. However, the optimization of chromatography pa- rameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multi- modal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R2 of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manu- facturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purifi- cation in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding

3D-printed micro bubble column reactor with integrated microsensors for biotechnological applications: from design to evaluation

With the technological advances in 3D printing technology, which are associated with ever-increasing printing resolution, additive manufacturing is now increasingly being used for rapid manufacturing of complex devices including microsystems development for laboratory applications. Personalized experimental devices or entire bioreactors of high complexity can be manufactured within few hours from start to finish. This study presents a customized 3D-printed micro bubble column reactor (3D-µBCR), which can be used for the cultivation of microorganisms (e.g., Saccharomyces cerevisiae) and allows online-monitoring of process parameters through integrated microsensor technology. The modular 3D-µBCR achieves rapid homogenization in less than 1 s and high oxygen transfer with kLa values up to 788 h-1 and is able to monitor biomass, pH, and DOT in the fluid phase, as well as CO2 and O2 in the gas phase. By extensive comparison of different reactor designs, the influence of the geometry on the resulting hydrodynamics was investigated. In order to quantify local flow patterns in the fluid, a three-dimensional and transient multiphase Computational Fluid Dynamics model was successfully developed and applied. The presented 3D-µBCR shows enormous potential for experimental parallelization and enables a high level of flexibility in reactor design, which can support versatile process development

3D-printed micro bubble column reactor with integrated microsensors for biotechnological applications: From design to evaluation

With the technological advances in 3D printing technology, which are associated with ever-increasing printing resolution, additive manufacturing is now increasingly being used for rapid manufacturing of complex devices including microsystems development for laboratory applications. Personalized experimental devices or entire bioreactors of high complexity can be manufactured within few hours from start to finish. This study presents a customized 3D-printed micro bubble column reactor (3D-µBCR), which can be used for the cultivation of microorganisms (e.g., Saccharomyces cerevisiae) and allows online-monitoring of process parameters through integrated microsensor technology. The modular 3D-µBCR achieves rapid homogenization in less than 1 s and high oxygen transfer with kLa values up to 788 h−1 and is able to monitor biomass, pH, and DOT in the fluid phase, as well as CO2 and O2 in the gas phase. By extensive comparison of different reactor designs, the influence of the geometry on the resulting hydrodynamics was investigated. In order to quantify local flow patterns in the fluid, a three-dimensional and transient multiphase Computational Fluid Dynamics model was successfully developed and applied. The presented 3D-µBCR shows enormous potential for experimental parallelization and enables a high level of flexibility in reactor design, which can support versatile process development. © 2021, The Author(s)

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure–property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs\u27 structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies

Towards recombinantly produced milk proteins: Physicochemical and emulsifying properties of engineered whey protein beta-lactoglobulin variants

DFG, 273937032, SPP 1934: Dispersitäts-, Struktur- und Phasenänderungen von Proteinen und biologischen Agglomeraten in biotechnologischen ProzessenBMBF, 031B0222, Basistechnologie Nachwuchsgruppe "Multiskalige Modellierung und Modifikation von Multienzymkomplexen als Basistechnologie für zellfreie Reaktionskaskaden" (II

Determination of intrinsic enzyme reaction kinetics using thermodynamic activities

Solvent selection plays a major role in economically competitive biocatalytic reaction systems. Until now, solvent selection relies on trial and error, since thermodynamic phenomena and kinetic effects during reactions are poorly understood. To allow for rational medium engineering, the effects of organic solvents on biocatalytic reaction systems have to be identified. These effects can be divided into (I) changes of the thermodynamic activities of the reactants and (II) changes of the intrinsic enzyme kinetic parameters. By combining high-throughput experimentation with reaction kinetic modeling and thermodynamic prediction, this work aims for the discrimination of thermodynamic and kinetic effects on oxidoreductase reaction in water-miscible monophasic organic solvents, in order to provide a basis for a rational choice of these solvents. The work can be divided into three packages. First, a thorough quantitative inactivation study is applied on Candida parapsilosis carbonyl reductase (CPCR2) and on Lactobacillus brevis alcohol dehydrogenase (LbADH) as model catalysts. Possible inactivation phenomena are quantified on microliter scale with respect to their effect on kinetic assays in order to derive a suitable mathematical inactivation model. For CPCR2, the results demonstrate that interface interactions and dimer dissociation are the main reasons for inactivation resulting in a complex inactivation scheme. For LbADH, tetramer dissociation has been observed at low protein concentration. This can be mathematically described by a first-order inactivation model. Second, reliable experimentation in micro-scale enzyme assays is required. Thus, typical problems occurring in MTPs such as temperature deviations or evaporation were evaluated. Internal thermal gradients within a polystyrene MTP of up to 2.2° C and even higher deviations from the set temperature were observed, which caused a variation in the corresponding enzyme activity of up to 20% and the deviations in relation to experiments in cuvettes by up to 40%. Subsequently, the reproducibility and precision of enzyme kinetic assays in different microliter scale systems were thoroughly analyzed. The initial reaction rates increased systematically from polystyrene MTPs to quartz MTPs to quartz cuvettes, whereas the experimental errors decreased in the same order (18 to 2%). While the microkinetic parameters vary up to an order of magnitude between different systems, the corresponding macrokinetic parameters lie in the same range for all systems varying only by up to a factor of 2 to 3.Third, MTBE was selected as model solvent providing reasonable LbADH stability and effect on thermodynamic activity as predicted using the COSMO-RS model. Kinetic experiments at both identical and purposefully different thermodynamic activities were used for kinetic modeling. Although MTBE seems to have an overall positive effect on the enzymatic reaction if examined on basis of concentration, solvation-corrected data suggest a negative effect of increasing MTBE concentration on the enzyme. It is assumed that MTBE hinders the intrinsic enzyme catalytic steps and compromises the performance of the enzyme, which is also indicated by the kinetic parameters. Overall, the importance of rational analysis and understanding of the experimental environment in biocatalysis is highlighted in this work. Important steps were taken to develop a proof of concept for kinetic modeling based on thermodynamic activities. This might provide better understanding of the behavior of biocatalysts in organic solvents. Solvent-enzyme interactions might be further elucidated rationally and, thereby, lead to a rational medium engineering