Model-based design space determination of peptide chromatographic purification processes

Operating a chemical process at fixed operating conditions often leads to suboptimal process performances. It is important in fact to be able to vary the process operating conditions depending upon possible changes in feed composition, products requirements or economics. This flexibility in the manufacturing process was facilitated by the publication of the PAT initiative from the U.S. FDA [1]. In this work, the implementation of Quality-by-design in the development of a chromatographic purification process is discussed. A procedure to determine the design space of the process using chromatographic modeling is presented. Moreover, the risk of batch failure and the critical process parameters (CPP) are assessed by modeling. The ideal cut strategy is adopted and therefore only yield and productivity are considered as critical quality attributes (CQA). The general trends in CQA variations within the design space are discussed. The effect of process disturbances is also considered. It is shown that process disturbances significantly decrease the design space and that only simultaneous and specific changes in multiple process parameters (i.e. critical process parameters (CPP) lead to batch failure. The reliability of the obtained results is proven by comparing the model predictions to suitable experimental data. The case study presented in this work proves the reliability of process development using a model-based approach.