Model-integrated process development demonstrated on the optimization of a robotic cation exchange step

• Published in: Chemical engineering science Vol. 76; pp. 129 - 139
• Main Authors: Osberghaus, A., Drechsel, K., Hansen, S., Hepbildikler, S.K., Nath, S., Haindl, M., von Lieres, E., Hubbuch, J.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 09.07.2012; Elsevier
• Subjects: agarose; Applied sciences; case studies; cation exchange; Chemical engineering; Chromatography; cytochrome c; Cytochromes; Downstream processing; Elution; Exact sciences and technology; experimental design; Flexibility; Ion exchange; lysozyme; Mathematical modeling; Mathematical models; mechanistic models; Optimization; prediction; Ribonuclease A; ribonucleases; Scale-up; Separation; Simulation; Mathematical modeling; Simulation; Optimization; Chromatography; Downstream processing; Scale-up; Cation exchange; Elution; Scale effect; Prediction; Retention; Flexibility; Modeling; Design; Extrapolation; Experimental design; Morphology; Miniaturized; pH; Robustness; Ion exchange
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2012.04.004

A new concept for chromatography process development based on high-through put data and mechanistic modeling will be presented in this paper. The concept is established in close cooperation between experimentation, modeling and model-based experimental design and allows for robustness analyses and upscale predictions. It will be demonstrated based on a case study: the optimization of a multicomponent separation (lysozyme, ribonuclease A and cytochrome c on SP Sepharose FFTM), subject to pH conditions and optimal settings for the shape of the elution gradient. Peak resolution and a precise prediction of retention times were chosen as performance variables in the case study to demonstrate the flexibility of the concept. It was shown that the concept of model-integrated process development is simple to perform from miniaturized scale on. The data, derived from model-based optimally designed experiments, provided sufficient information for process development, the model was calibrated and predictions for optimal separation setups as well as for the upscale showed a high precision. Consequently, the accumulation of data from high-throughput screenings can be used profitably for model-based process optimization and upscale predictions. ► Mechanistic modeling based on HTS data for chromatographic separation is introduced. ► An empiric (RSM) and a mechanistic approach for process optimization are compared. ► A thorough evaluation of advantages and disadvantages of both methods is given. ► Mechanistic modeling is the method of choice for optimization and upscale prediction. ► Consequently, a concept of model-integrated process development is introduced.