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. This was achieved by developing mechanistic models of the reaction and SEC steps, establishing the causal links between process conditions and product quality. Model analysis was used to complement the qualitative risk assessment, and design space and critical process parameters were identified. The simulation results gave an experimental plan focusing on the "worst-case regions" in terms of product quality and yield. In this way, the experiments could be used to verify both the suggested process and the model results. This work demonstrates the necessary steps of model-assisted process analysis, from model development through experimental verification. Biotechnol. Bioeng. © 2013 Wiley Periodicals, Inc

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 in to the body. The aim of the 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. The equilibrium parameter was found to be 272 M(-1) and the reaction kinetic parameter 1.33·10(-5) s(-1) , both within the 95%percnt; confidence interval of the results obtained in the previous work. The effective diffusivities were estimated to be 1.45·10(-13) and 1.90·10(-14) m(2) /s for the monomers and dimers, respectively. Good agreement was found between the new model and the chromatograms obtained in the SEC experiments. The model was also able to predict the decrease of dimers due to the dilution and separation in the SEC column during long retention times

Prediction of IgG1 aggregation in solution.

Interest in monoclonal antibody aggregation is increasing as aggregates of biopharma-ceuticals 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 4.5 to 5.5, salt concentrations from 100 to 600 mmol/kg and protein concentrations of 10.6 to 26.3 g/l; samples were taken at intervals of 20 minutes to five hours over time periods from 4 hours to 7.6 days, and analyzed with size-exclusion chromatography. The experiments showed the formation of dimers from monomers, but no higher order aggregates. Dilution of samples containing dimers led to the reversal of the dimerization reaction. Measurements of the concentrations of each component were made by fitting exponentially modified Gaussian peaks to the chromatograms used to measure the concentrations of the different forms of protein. This stoichiometric reaction model was able to predict the formation of dimers by the antibody studied. The equilibrium constant was found to be dependent on the salt concentration, and the kinetic constant showed a dependence on the pH of the solution. The prediction of the aggregation leads to a possibility of optimizing the conditions in order to prevent the dimer formation and to maximize the monomer concentration

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 in to the body. The aim of the 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. The equilibrium parameter was found to be 272 M(-1) and the reaction kinetic parameter 1.33·10(-5) s(-1) , both within the 95%percnt; confidence interval of the results obtained in the previous work. The effective diffusivities were estimated to be 1.45·10(-13) and 1.90·10(-14) m(2) /s for the monomers and dimers, respectively. Good agreement was found between the new model and the chromatograms obtained in the SEC experiments. The model was also able to predict the decrease of dimers due to the dilution and separation in the SEC column during long retention times

Prediction of IgG1 aggregation in solution.

Interest in monoclonal antibody aggregation is increasing as aggregates of biopharma-ceuticals 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 4.5 to 5.5, salt concentrations from 100 to 600 mmol/kg and protein concentrations of 10.6 to 26.3 g/l; samples were taken at intervals of 20 minutes to five hours over time periods from 4 hours to 7.6 days, and analyzed with size-exclusion chromatography. The experiments showed the formation of dimers from monomers, but no higher order aggregates. Dilution of samples containing dimers led to the reversal of the dimerization reaction. Measurements of the concentrations of each component were made by fitting exponentially modified Gaussian peaks to the chromatograms used to measure the concentrations of the different forms of protein. This stoichiometric reaction model was able to predict the formation of dimers by the antibody studied. The equilibrium constant was found to be dependent on the salt concentration, and the kinetic constant showed a dependence on the pH of the solution. The prediction of the aggregation leads to a possibility of optimizing the conditions in order to prevent the dimer formation and to maximize the monomer concentration

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. Optimizing both steps at the same time makes it possible for the optimization method to take into account that the di-PEGylated protein is much easier to separate than the non-PEGylated protein. The easier separation makes it possible to get a higher yield and productivity at the same time. The effect of recycling was also studied and the yield could be increased by 30 % by recycling the unreacted protein. However, if maximum productivity is required, batch mode is preferable