Raman-Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Fermentations.

Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in fermentations has been hindered by the inherent strong fluorescence background from the cells.

Industrial ion-exchange chromatography development using discontinuous Galerkin methods coupled with forward sensitivity analysis.

In this work, a discontinuous Galerkin method coupled with forward sensitivity analysis (DG-FSA) is presented. The DG-FSA method is used to reduce computational cost required for model-based ion-exchange chromatography development using industrial load samples. As an example, the design of an anion-exchange chromatography step is considered.

Prediction of reversible IgG1 aggregation occurring in a size exclusion chromatography column is enabled through a model based approach.

One important aspect of antibody separation being studied today is aggregation, as this not only leads to a loss in yield, but aggregates can also be hazardous if injected into the body. The aim of this study was to determine whether the methodology applied in the previous study could be used to predict the aggregation of a different batch of IgG1, and to model the aggregation occurring in a SEC column. Aggregation was found to be reversible.

Prediction of IgG1 aggregation in solution.

Interest in monoclonal antibody aggregation is increasing as aggregates of biopharmaceuticals can cause an immunogenic response when injected into the body. In this work, a stoichiometric reaction model from concentration-time data is developed to predict the dimer ratio in stored antibody solutions over time. IgG1 was incubated at pH from 4.

Model-based design and integration of a two-step biopharmaceutical production process.

This paper presents the design of a two-step process in which the first step is PEGylation of a protein, and the second step is chromatographic purification of the target mono-PEGylated protein from the unreacted and the di-PEGylated impurities. The difference between optimizing each process step separately and optimizing them simultaneously is studied. It was found that by optimizing the steps simultaneously up to a 100 % increase in productivity could be obtained without reduction in yield.

Model-based risk analysis of coupled process steps.

A section of a biopharmaceutical manufacturing process involving the enzymatic coupling of a polymer to a therapeutic protein was characterized with regards to the process parameter sensitivity and design space. To minimize the formation of unwanted by-products in the enzymatic reaction, the substrate was added in small amounts and unreacted protein was separated using size-exclusion chromatography (SEC) and recycled to the reactor. The quality of the final recovered product was thus a result of the conditions in both the reactor and the SEC, and a design space had to be established for both processes together.