Cross-scale quality assessment of a mechanistic cation exchange chromatography model.

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

Prediction uncertainty assessment of chromatography models using Bayesian inference.

Mechanistic modeling of chromatography has been around in academia for decades and has gained increased support in pharmaceutical companies in recent years. Despite the large number of published successful applications, process development in the pharmaceutical industry today still does not fully benefit from a systematic mechanistic model-based approach. The hesitation on the part of industry to systematically apply mechanistic models can often be attributed to the absence of a general approach for determining if a model is qualified to support decision making in process development. In this work a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development.

An integrated precipitation and ion-exchange chromatography process for antibody manufacturing: Process development strategy and continuous chromatography exploration.

In the past decades, research was carried out to find cost-efficient alternatives to Protein A chromatography as a capture step in monoclonal antibody (mAb) purification processes. In this work, polyethylene glycol (PEG) precipitation has shown promising results in the case of mAb yield and purity. Especially with respect to continuous processing, PEG precipitation has many advantages, like low cost of goods, simple setup, easy scalability, and the option to handle perfusion reactors. Nevertheless, replacing Protein A has the disadvantage of renouncing a platform unit operation as well. Furthermore, PEG precipitation is not capable of reducing high molecular weight impurities (HMW) like aggregates or DNA. To overcome these challenges, an integrated process strategy combining PEG precipitation with cation-exchange chromatography (CEX) for purification of a mAb is presented. This work discusses the process strategy as well as the associated fast, easy, and material-saving process development platform. These were implemented through the combination of high-throughput methods with empirical and mechanistic modeling. The strategy allows the development of a common batch process. Additionally, it is feasible to develop a continuous process. In the presented case study, a mAb provided from cell culture fluid (HCCF) was purified. The precipitation and resolubilization conditions as well as the chromatography method were optimized, and the mutual influence of all steps was investigated. A mAb yield of over 95.0% and a host cell protein (HCP) reduction of over 99.0% could be shown. At the same time, the aggregate level was reduced from 3.12% to 1.20% and the DNA level was reduced by five orders of magnitude. Furthermore, the mAb was concentrated three times to a final concentration of 11.9mg/mL.

Feasibility of using continuous chromatography in downstream processing: Comparison of costs and product quality for a hybrid process vs. a conventional batch process.

The protein A capture step is the main cost-driver in downstream processing, with high attrition costs especially when using protein A resin not until end of resin lifetime. Here we describe a feasibility study, transferring a batch downstream process to a hybrid process, aimed at replacing batch protein A capture chromatography with a continuous capture step, while leaving the polishing steps unchanged to minimize required process adaptations compared to a batch process. 35g of antibody were purified using the hybrid approach, resulting in comparable product quality and step yield compared to the batch process. Productivity for the protein A step could be increased up to 420%, reducing buffer amounts by 30-40% and showing robustness for at least 48h continuous run time. Additionally, to enable its potential application in a clinical trial manufacturing environment cost of goods were compared for the protein A step between hybrid process and batch process, showing a 300% cost reduction, depending on processed volumes and batch cycles.

Strategy for assessment of the colloidal and biological stability of H1N1 influenza A viruses.

Current influenza vaccines are mostly formulated as liquids which requires a continuous cold chain to maintain the stability of the antigen. For development of vaccines with an increased stability at ambient temperatures, manifold parameters and their influences on the colloidal stability and activity of the antigen have to be understood. This work presents a strategy to examine both, the colloidal stability and the remaining biological activity of H1N1 influenza viruses under various conditions after an incubation of 40 days. H1N1 phase diagrams were generated for several pH values and different initial H1N1 and NaCl concentrations. It was shown that the highest H1N1 recoveries were obtained for pH 6 and that moderate amounts of NaCl are favorable for increased recoveries. In contrast to colloidal stability, the highest remaining HA activity was observed at pH 9. The electrostatic and hydrophobic surface properties of H1N1 were investigated to reveal the mechanisms accounting for the decrease in stability. Secondly, the capability of virus precipitation by polyethylene glycol in combination with determination of surface hydrophobicity was proven to be useful as a predictive tool to rank stability under different conditions. This methodology enables the rapid assessment of aggregation propensity of H1N1 formulations and the influence on the activity of the virus particles and might become a standard tool during the development of vaccine formulations.

Next generation vaccines and vectors: Designing downstream processes for recombinant protein-based virus-like particles.

In recent years, the development of novel recombinant virus-like particles (VLPs) has been generating new perspectives for the prevention of untreated and arising infectious diseases. However, cost-reduction and acceleration of manufacturing processes for VLP-based vaccines or vectors are key challenges for the global health system. In particular, the design of rapid and cost-efficient purification processes is a critical bottleneck. In this review, we describe and evaluate new concepts, development strategies and unit operations for the downstream processing of VLPs. A special focus is placed on purity requirements and current trends, as well as chances and limitations of novel technologies. The discussed methods and case studies demonstrate the advances and remaining challenges in both rational process development and purification tools for large biomolecules. The potential of a new era of VLP-based products is highlighted by the progress of various VLPs in clinical phases.

Robust high-throughput batch screening method in 384-well format with optical in-line resin quantification.

High-throughput batch screening technologies have become an important tool in downstream process development. Although continuative miniaturization saves time and sample consumption, there is yet no screening process described in the 384-well microplate format. Several processes are established in the 96-well dimension to investigate protein-adsorbent interactions, utilizing between 6.8 and 50 µL resin per well. However, as sample consumption scales with resin volumes and throughput scales with experiments per microplate, they are limited in costs and saved time. In this work, a new method for in-well resin quantification by optical means, applicable in the 384-well format, and resin volumes as small as 0.1 µL is introduced. A HTS batch isotherm process is described, utilizing this new method in combination with optical sample volume quantification for screening of isotherm parameters in 384-well microplates. Results are qualified by confidence bounds determined by bootstrap analysis and a comprehensive Monte Carlo study of error propagation. This new approach opens the door to a variety of screening processes in the 384-well format on HTS stations, higher quality screening data and an increase in throughput.

A comprehensive molecular dynamics approach to protein retention modeling in ion exchange chromatography.

In downstream processing, the underlying adsorption mechanism of biomolecules to adsorbent material are still subject of extensive research. One approach to more mechanistic understanding is simulating this adsorption process and hereby the possibility to identify the parameters with strongest impact. So far this method was applied with all-atom molecular dynamics simulations of two model proteins on one cation exchanger. In this work we developed a molecular dynamics tool to simulate protein-adsorber interaction for various proteins on an anion exchanger and ran gradient elution experiments to relate the simulation results to experimental data. We were able to show that simulation results yield similar results as experimental data regarding retention behavior as well as binding orientation. We could identify arginines in case of cation exchangers and aspartic acids in case of anion exchangers as major contributors to binding.