Model‐based monitoring of industrial reversed phase chromatography to predict insulin variants

Abstract

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process‐ and product‐related impurities. However, removing product‐related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model‐based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model‐based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model‐based monitoring in downstream polishing at industrial scale to take pooling decisions.

Monitoring of product variants in biopharmaceutical downstream processing : Mechanistic and data-driven modeling approaches

“…e pooling decisions taken by the mechanistic model-based monitoring tool are indicated in Figure 5.2b and compared to current practice pooling. A yield increase of 4.7 % was revealed [83]. By relying on mechanistic model-based monitoring, process downtime can potentially be decreased, and the product yield can be increased while ensuring product purity.…”

“…Structure of mechanistic model to calibrate model parameters to experimental data (adapted from[83]). …”