Radial scale-down of packed bed chromatography in a thin cylindrical tube for preparative media

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.

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.

A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity

Within the Quality by Design (QbD) framework proposed by the International Conference on Harmonisation (ICH), high-throughput process development (HTPD) and mechanistic modeling are of outstanding importance for future biopharmaceutical chromatography process development. In order to compare the data derived from different column scales or batch chromatographies, the amount of adsorber has to be quantified with the same noninvasive method. Similarly, an important requirement for the implementation of mechanistic modeling is the reliable determination of column characteristics such as the ionic capacity Λ for ion-exchange chromatography with the same method at all scales and formats. We developed a method to determine the ionic capacity in column and batch chromatography, based on the adsorption/desorption of the natural, uv-detectable amino acid histidine. In column chromatography, this method produces results comparable to those of classical acid-base titration. In contrast to acid-base titration, this method can be adapted to robotic batch chromatographic experiments. We are able to convert the adsorber volumes in batch chromatography to the equivalent volume of a compressed column. In a case study, we demonstrate that this method increases the quality of SMA parameters fitted to batch adsorption isotherms, and the capability to predict column breakthrough experiments. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:666-677, 2016.