The development of chromatographic purification processes for therapeutic proteins is mainly performed using experimental data from laboratory scale. This allows time, cost and material effective process optimization and thorough assessment of robustness with respect to process input parameters. However, there is only limited experimental data available on how the process performs at manufacturing scale. Thus transferring the developed process to production facilities is not always straightforward and can be challenging. Mechanistic modeling of chromatography provides the opportunity to simulate the process performance at manufacturing scale and generate data on scale-up performance. Models can be calibrated using laboratory scale data from process development. The model then is used to simulate the process for manufacturing scale conditions, also considering potential restrictions due to characteristics of the production facility. We tested this in silico scale-up approach using laboratory and GMP manufacturing scale chromatography data of a monoclonal antibody. Steric mass action models in combination with the modeling software ChromX were used for the description and simulation of an ion exchange chromatography step in downstream processing. The model was first calibrated using chromatography runs from laboratory scale experiments. Model calibration incorporated the impact of process parameters like buffer conductivity, pH, impurity composition and load density on the separation performance. Having established a mechanistic model an in silico scale-up was performed by adjusting the chromatography system and column run parameters to manufacturing scale. Comparing model predictions for manufacturing runs from the in silico scale-up model with GMP production data showed good agreement for process outputs and quality attributes like purity and yield. Our results show that combining scale-down experimental data and mechanistic modeling provides the opportunity to assess in silico process performance, robustness and facility fit for manufacturing scale already at an early process development phase. This will ensure an efficient scale-up to commercial production scales during late stage transfer activities, but also support trouble shooting activities for marketed products.

In silico scale-up from laboratory to production scale: A case study on applying mechanistic modeling for describing chromatographic purification of a monoclonal antibody

253rd National Meeting (2017)

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