Purification of monoclonal antibodies (mAbs) is a complex process depending on raw material attributes and critical quality attributes. Chromatography plays a crucial role in purification of mAbs. In order to obtain an efficient chromatographic purification process in lesser time and which consistently gives product of targeted yield and product purity, Design of Experiments (DOE) based process optimization offers a solution. Though DOE is useful in a typical bioprocess but in a typical bioprocess, input and output variables large in numbers and difficult to model and in general large number of experiments are required if we proceed in tradition way. In this poster, we demonstrate how to handle the above problem by splitting the DOE designs in two steps. The first step included optimizing the most important factor that impacts the process maximum and second step included response surface design to fine tune other important process parameters. Overall, use of a standalone DOE or a single big DOE is not recommended. DOE along with mechanistic understanding or step wise development of DOE designs ease up empirical model development with less number of experiments and thus save lot of resources and reduces process development cycle time.<br/>The second important problem faced in case of partial chromatographic separation such as product aggregate and variants and when product pooling is required, output response variable is difficult to model. In this work we have demonstrated modeling of such response variables through an empirical fit and then using the fitting parameters in DOE as response variables to build the final empirical model. This not only eased up the process of the model development but also captured the entire output response from the process.<br/>There two problems have been demonstrated through two case studies (a) Case study I: hydrophobic interaction chromatography for removal of  high molecular weight (HMW) aggregates and (b) Case study II: Cation exchange chromatography  for separation of charge variants and HMW aggregates.<br/>

Design of experiments applications in bioprocessing: Chromatography process development

253rd National Meeting (2017)

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