Simulation of the dynamic packing behavior of preparative chromatography columns via discrete particle modeling

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Automatic 3D cluster modelling of COVID-19 through voxel-based redistribution

Computational analysis of virus dynamics provides a non-contact environment for the study of the vital object. Cluster modelling is an essential step to investigate the properties of a group of viruses, and an automatic approach is required for massive 3D data processing. The morphological complexity of individual virus limits the application of smooth function algorithms with a regular-shaped assumption. This paper proposed a voxel-based redistribution approach to generate the virus cluster with COVID-19 input automatically. Representative elementary volume analysis was performed to address the statistical influence from the digital sample size. Coordination number analysis and surface density measurement were conducted with COVID-19 input and spherical input for comparison. The proposed approach is in natural compatibility with the lattice Boltzmann method for fluid dynamics analysis. A virtual permeation simulation was performed with the COVID-19 cluster and spherical cluster to demonstrate the necessity to include spike protein structure in the cluster modelling.

Improved packing of preparative biochromatography columns by mechanical vibration

The bioprocessing industry relies on packed-bed column chromatography as its primary separation process to attain the required high product purities and fulfill the strict requirements from regulatory bodies. Conventional column packing methods rely on flow packing and/or mechanical compression. In this work, the application of ultrasound and mechanical vibration during packing was studied with respect to packing density and homogeneity. We investigated two widely used biochromatography media, incompressible ceramic hydroxyapatite, and compressible polymethacrylate-based particles, packed in a laboratory-scale column with an inner diameter of 50 mm. It was shown that ultrasonic irradiation led to reduced particle segregation during sedimentation of a homogenized slurry of polymethacrylate particles. However, the application of ultrasound did not lead to an improved microstructure of already packed columns due to the low volumetric energy input (~152 W/L) caused by high acoustic reflection losses. In contrast, the application of pneumatic mechanical vibration led to considerable improvements. Flow-decoupled axial linear vibration was most suitable at a volumetric force output of ~1,190 N/L. In the case of the ceramic hydroxyapatite particles, a 13% further decrease of the packing height was achieved and the reduced height equivalent to a theoretical plate (rHETP) was decreased by 44%. For the polymethacrylate particles, a 18% further packing consolidation was achieved and the rHETP was reduced by 25%. Hence, it was shown that applying mechanical vibration resulted in more efficiently packed columns. The application of vibration furthermore is potentially suitable for in situ elimination of flow channels near the column wall.