Trend analysis of performance parameters of pre-packed columns for protein chromatography over a time span of ten years

Factors affecting therapeutic protein purity and yield during chromatographic purification

Therapeutic proteins are recombinant proteins generated through recombinant DNA technology and have attracted a great deal of interest in numerous applications, including pharmaceutical, cosmetic, human and animal health, agriculture, food, and bioremediation. Producing therapeutic proteins on a large scale, mainly in the pharmaceutical industry, necessitates a cost-effective, straightforward, and adequate manufacturing process. In industry, a protein separation technique based mainly on protein characteristics and modes of chromatography will be applied to optimize the purification process. Typically, the downstream process of biopharmaceutical operations may involve multiple chromatography phases that require the use of large columns pre-packed with resins that must be inspected before use. Approximately 20% of the proteins are assumed to be lost at each purification stage during the production of biotherapeutic products. Hence, to produce a high quality product, particularly in the pharmaceutical industry, the correct approach and understanding of the factors influencing purity and yield during purification are necessary.

Scalable, Robust and Highly Productive Novel Convecdiff Membrane Platform for mAb Capture

The recombinant monoclonal antibody capture step represents the current bottleneck in downstream processing. Protein A resins are diffusion-limited chromatography materials which require low flow rates to achieve a binding capacity above 30 g L⁻¹ with the result of low productivity. Here, we present a novel chromatography membrane combining superior binding capacities with high flow rates for high productivity while achieving comparable product quality as state-of-the-art protein A resins. Further, we demonstrate full scalability of this convecdiff technology with experimental data demonstrating suitability for bioprocessing at different scales. This technology results in more than 10-fold higher productivity compared to Protein A resins, which is maintained during scale up. We demonstrate the influence of residence times, feed titers and the cleaning regime on productivity and indicate optimal utilization of the convecdiff membrane based on feed titer availability. The underlying high productivity and short cycle times of this material enable the purification of monoclonal antibodies with 10-times less chromatography material used per batch and utilization of the membrane within one batch. Provided in disposable consumables, this novel technology will remove column handling in bioprocesses and resin re-use over multiple batches.

Transformation of Biopharmaceutical Manufacturing Through Single-Use Technologies: Current State, Remaining Challenges, and Future Development

Single-use technologies have transformed conventional biopharmaceutical manufacturing, and their adoption is increasing rapidly for emerging applications like antibody-drug conjugates and cell and gene therapy products. These disruptive technologies have also had a significant impact during the coronavirus disease 2019 pandemic, helping to advance process development to enable the manufacturing of new monoclonal antibody therapies and vaccines. Single-use systems provide closed plug-and-play solutions and enable process intensification and continuous processing. Several challenges remain, providing opportunities to advance single-use sensors and their integration with single-use systems, to develop novel plastic materials, and to standardize design for interchangeability. Because the industry is changing rapidly, a holistic analysis of the current single-use technologies is required, with a summary of the latest advancements in materials science and the implementation of these technologies in end-to-end bioprocesses.

Prediction of the performance of pre-packed purification columns through machine learning

Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90 and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, for example, by focusing on improvements of backbone and functional mode to obtain optimized packings.

A cuboid chromatography device having short bed-height gives better protein separation at a significantly lower pressure drop than a taller column having the same bed-volume

Simultaneously reducing the bed-height and increasing the area of cross-section, while keeping the bed-volume the same, would substantially reduce the pressure drop across a process chromatography column. This would minimize problems such as resin compaction and non-uniformity in column packing, which are commonly faced when using soft chromatographic media. However, the increase in macroscale convective dispersion due to the increase in column diameter, and the resultant loss in resolution would far outweigh any potential benefit. Cuboid-packed bed devices have lower macroscale convective dispersion compared to their equivalent cylindrical columns. In this paper, we discuss how and why a flat cuboid chromatography device having a short bed-height gives better protein separation, at a significantly lower pressure drop, than a taller column having the same bed-volume. First, we explored this option based on computational fluid dynamic (CFD) simulations. Depending on the flow rate, the pressure drop across the flat cuboid device was lower than that in the tall column by a factor of 6.35 to 6.4 (i.e. less than 1/6^th the pressure). The CFD results also confirmed that the macroscale convective dispersion within the flat cuboid device was significantly lower. Head-to-head separation experiments using a 1 mL flat cuboid device having a bed-height of 10 mm, and a 1 mL tall column having a bed-height of 25.8 mm, both packed with the same chromatographic media, were carried out. The number of theoretical plates per unit bed-height was on an average, around 2.5 time times greater with the flat cuboid device, while the total number of theoretical plates in the two devices were comparable. At any given superficial velocity, the height equivalent of a theoretical plate in the tall column was on an average, higher by a factor 2.5. Binary protein separation experiments showed that at any given flow rate, the resolution obtained using the flat cuboid device was significantly higher than that obtained with the tall column. This work opens up the possibility of designing and developing short bed-height chromatography devices for carrying out high-resolution biopharmaceutical purifications, at very low pressures.

Pressure-Flow experiments, packing, and modeling for scale-up of a mixed mode chromatography column for biopharmaceutical manufacturing

To obtain consistent chromatographic behavior, it is important to develop resin packing methods in accordance with the characteristics of each resin. Resins, particularly those with a significant level of compressibility, require proper knowledge of the packing methodology to ensure scalable performance. The study demonstrates the applicability of pressure-flow modeling based on the Blake-Kozeny equation for cellulose based resins, using the MEP HyperCel (Pall) resin as a case study. This approach enabled the understanding of the appropriate bed compressibility and the determination of the minimum column diameter that can predict bed integrity during commercial manufacturing scale operation. Studies suggested that scale-dependent wall effects become negligible for column diameters exceeding 20 cm. Pressure-flow modeling produced a minimum compression recommendation of 0.206 for the MEP HyperCel resin. Columns with diameters up to 80 cm packed with this bed compression yielded incompressible beds with pressure-flow curves consistent with model predictions. Model parameter (particle diameter, viscosity, porosity) values were then varied to demonstrate how changing operating conditions influence model predictions. This analysis supported the successful troubleshooting of unexpected high pressures at the commercial manufacturing scale using MEP HyperCel resin, further supporting the applicability of this approach.

Progression of continuous downstream processing of monoclonal antibodies: Current trends and challenges

Rapid advances in intensifying upstream processes for biologics production have left downstream processing as a bottleneck in the manufacturing scheme. Biomanufacturers are pursuing continuous downstream process development to increase efficiency and flexibility, reduce footprint and cost of goods, and improve product consistency and quality. Even after successful laboratory trials, the implementation of a continuous process at manufacturing scale is not easy to achieve. This paper reviews specific challenges in converting each downstream unit operation to a continuous mode. Key elements of developing practical strategies for overcoming these challenges are detailed. These include equipment valve complexity, favorable column aspect ratio, protein-A resin selection, quantitative assessment of chromatogram peak size and shape, holistic process characterization approach, and a customized process economic evaluation. Overall, this study provides a comprehensive review of current trends and the path forward for implementing continuous downstream processing at the manufacturing scale.