Adsorber Quantification in Batch and Column Chromatography

The dream team to comply with Quality by Design: High throughput process development techniques and process modeling.

Figure: Batch adsorption isotherm, based on the adsorber skeleton volume. Orange and blue crosses show the measured data without and with histidin correction, respectively. The curves describe the SMA isotherms fitted to the data (without histidine correction: orange, with histidine correction: blue).

Quality by Design demands can be fulfilled by mechanistic process understanding and/or a massive expansion of the data basis – for example by high throughput techniques such as batch chromatography. Mechanistic models strongly depend on model parameters, which are fitted from experimental data, which are typically taken from column runs at lab-scale.

Quantification of adsorber characteristics: column scale vs. batch systems.  To improve the implementation of a process at different stages during its development, the same model should be applied for all scales and formats. To allow for a model-based scale comparison, precise and uniform measurements of different column characteristics such as ionic capacity are then necessary. The standard technique to determine the ionic capacity in column scale is the acid-base-titration. As this approach requires in-line conductivity trace, the method is not applicable for high throughput experimental systems like batch chromatography.

Measuring histidine capacity to determine ionic capacity. An alternative method which can be used in both, column and batch chromatography, is the measurement of the total histidine capacity. The results of this non-invasive photometric method are comparable to conventional acid-base-titrations in column scale.

Improved prediction of breakthrough using histidine corrected parameters. By means of the presented method the batch adsorber volumes could be corrected to generate more precise modeling parameters. Implementing these parameters, a column breakthrough curve could be modeled in ChromX from a single condition batch adsorption isotherm. Additionally the presented case study can be extended to batch kinetics and batch bind-and-elute experiments. With this approach, batch data will enhance its role in conventional and in silico process development.

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